A Unified Theory of Relativistic Optical-Acoustic Future Prediction & Meta-Consensus
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Created at: 2026-06-22 01:49:22
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<title>A Unified Theory of Relativistic Optical-Acoustic Future Prediction & Meta-Consensus</title>
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<h1>A Unified Theory of Relativistic Optical-Acoustic Future Prediction & Meta-Consensus</h1>
<div class="abstract">
<strong>Abstract:</strong> This paper formalizes the mathematical framework for a unified multi-medium observation system. By combining gravitational time-dilation lenses (for optical photons) with acoustic entropy density nets, the system can predict future states of physical phenomena. The framework includes rigorous definitions for information curvature, observation momentum, causal syndication limits, ethical risk operators, and decision activation thresholds. This document provides the long-form mathematical proofs, comprehensive symbol definitions, and a testable Proof-of-Concept simulation demonstrating how manipulating lens mass can alter predictive outcomes.
</div>
<!-- PART I: OPTICAL INCEPTION -->
<div class="phase-block">
<h2>Part I: Optical Inception & Gravitational Time Dilation</h2>
<p>The system begins with a raw photon emission at the Deep Space Outdoor Observatory. This optical model utilizes an engineered "Near-Singularity Meta-Lens" to warp spacetime, inducing a measurable Shapiro delay to allow the system to "see" future states ahead of classical propagation.</p>
<div class="equation-box">
<strong>(1.1) Initial Photon Wavefunction:</strong>
\[ \Psi_0(\mathbf{x}, t_0) = \int_{-\infty}^{\infty} A(\omega) e^{i(\mathbf{k} \cdot \mathbf{x} - \omega t_0)} d\omega \]
<br>
<strong>(1.2) Gravitational Time Dilation Factor (Lorentz Factor):</strong>
\[ \gamma = \sqrt{ 1 - \frac{2 G M_{\text{lens}}}{c^2 r_{\text{eff}}} } \]
<br>
<strong>(1.3) Gravitational Deflection Angle:</strong>
\[ \theta = \frac{4 G M_{\text{lens}}}{c^2 r_{\text{eff}}} \]
<br>
<strong>(1.4) Optical Lens Convolution Operator:</strong>
\[ \mathcal{O}_{\text{lens}}[\Psi_0] = \int_{\text{lens volume}} \gamma \left( \nabla^2 \Psi_0 - \frac{m_{\gamma}^2 c^2}{\hbar^2} \Psi_0 \right) d^3x \]
<br>
<strong>(1.5) Gravitational Shapiro Delay (Time Advance):</strong>
\[ \Delta t = \frac{2 G M_{\text{lens}}}{c^3} \ln\left( 1 + \frac{D_{\text{source}}}{r_{\text{eff}}} \right) \]
</div>
<div class="symbol-grid">
<div class="symbol-item"><div class="symbol-char">\(\Psi_0\)</div><div class="symbol-desc">Initial complex optical wavefunction of the incoming photon.</div></div>
<div class="symbol-item"><div class="symbol-char">\(A(\omega)\)</div><div class="symbol-desc">Spectral amplitude distribution of the source light.</div></div>
<div class="symbol-item"><div class="symbol-char">\(\gamma\)</div><div class="symbol-desc">Dimensionless Lorentz factor representing physical time dilation.</div></div>
<div class="symbol-item"><div class="symbol-char">\(G\)</div><div class="symbol-desc">Newton's universal gravitational constant (\(6.674 \times 10^{-11} \text{ m}^3 \text{ kg}^{-1} \text{ s}^{-2}\)).</div></div>
<div class="symbol-item"><div class="symbol-char">\(M_{\text{lens}}\)</div><div class="symbol-desc">The effective mass of the engineered meta-lens (kg).</div></div>
<div class="symbol-item"><div class="symbol-char">\(r_{\text{eff}}\)</div><div class="symbol-desc">The radial distance from the singularity center to the photon's path.</div></div>
<div class="symbol-item"><div class="symbol-char">\(c\)</div><div class="symbol-desc">Speed of light in a vacuum.</div></div>
<div class="symbol-item"><div class="symbol-char">\(\mathcal{O}_{\text{lens}}\)</div><div class="symbol-desc">The physical operator transforming raw light into a dilated spacetime state.</div></div>
<div class="symbol-item"><div class="symbol-char">\(\Delta t\)</div><div class="symbol-desc">The time delay induced by the lens before the photon reaches the observer.</div></div>
<div class="symbol-item"><div class="symbol-char">\(D_{\text{source}}\)</div><div class="symbol-desc">The initial distance from the original light source to the lens.</div></div>
</div>
</div>
<!-- PART II: ROUTER MACRO MODULATION -->
<div class="phase-block">
<h2>Part II: Router Syndication & Macro-Driven Data Modulation</h2>
<p>The time-dilated photon is translated into a digital pulse via a CMOS sensor. The "HP The Machine" central processing unit selects a specific Macro function (similar to a pizza-robot recipe) to determine how the incoming data is filtered, allowing the system to switch between deep-space observation, generative gaming, or communication tasks.</p>
<div class="equation-box">
<strong>(2.1) Data Modulation Stream:</strong>
\[ S_{\text{data}}(t) = [ L(t) \cdot \mathcal{M}_{\text{macro}}(t) \cdot \alpha_{\text{freq}} ] \oplus \mathcal{B}_{\text{inject}}(t) \]
</div>
<div class="symbol-grid">
<div class="symbol-item"><div class="symbol-char">\(S_{\text{data}}(t)\)</div><div class="symbol-desc">The final modulated digital bit-stream entering the prediction engine.</div></div>
<div class="symbol-item"><div class="symbol-char">\(L(t)\)</div><div class="symbol-desc">Raw optical data stream translated from the photon pulse.</div></div>
<div class="symbol-item"><div class="symbol-char">\(\mathcal{M}_{\text{macro}}(t)\)</div><div class="symbol-desc">The dynamic Macro selection filter dictating the data's intended usage.</div></div>
<div class="symbol-item"><div class="symbol-char">\(\alpha_{\text{freq}}\)</div><div class="symbol-desc">The frequency modulation scaling factor.</div></div>
<div class="symbol-item"><div class="symbol-char">\(\oplus\)</div><div class="symbol-desc">The XOR bitwise operator for injecting external data without corrupting the original stream.</div></div>
<div class="symbol-item"><div class="symbol-char">\(\mathcal{B}_{\text{inject}}(t)\)</div><div class="symbol-desc">The "novel bit addition" function (wireless signals, memory loaders).</div></div>
</div>
</div>
<!-- PART III: ACOUSTIC DISTRIBUTION & OBSERVATION META -->
<div class="phase-block">
<h2>Part III: Acoustic Distribution & Meta-Observation Network</h2>
<p>The optical network feeds into a secondary acoustic observation platform. Here, sound phenomena \(P(x,y,z,t)\) propagate through a noise field \(N\). The newly introduced Meta-Observation layers (Information Curvature, Observation Density, Momentum, and Acceleration) characterize the integrity and speed of the acoustic environment.</p>
<div class="equation-box">
<strong>(3.1) Acoustic Information Curvature:</strong>
\[ \kappa_I = \int_{\mathcal{V}_M} \frac{d^2 I}{d \mathbf{r}^2} \cdot \nabla \rho_m(\mathbf{r}) \, d^3r \]
<br>
<strong>(3.2) Observation Density:</strong>
\[ \rho_O = \sum_{i=1}^{n} \frac{N_i}{V_O} \cdot \delta(t - t_i) \]
<br>
<strong>(3.3) Observation Entropy (Shannon):</strong>
\[ H_O = - \sum_{j=1}^{m} p_j \log_2 p_j \]
<br>
<strong>(3.4) Observation Momentum & Acceleration:</strong>
\[ M_O = \frac{d}{dt} \left( \int L(t) \cdot \mathcal{M}_{\text{macro}}(t) \, dt \right), \quad A_O = \frac{d^2}{dt^2} \left( \int L(t) \, dt \right) \]
</div>
<div class="symbol-grid">
<div class="symbol-item"><div class="symbol-char">\(\kappa_I\)</div><div class="symbol-desc">Acoustic Information Curvature. Defines the spatial physical distortion of sound waves.</div></div>
<div class="symbol-item"><div class="symbol-char">\(\mathcal{V}_M\)</div><div class="symbol-desc">The total physical volume of the acoustic transmission medium.</div></div>
<div class="symbol-item"><div class="symbol-char">\(\rho_O\)</div><div class="symbol-desc">Observation Density. The number of active detecting nodes per unit volume.</div></div>
<div class="symbol-item"><div class="symbol-char">\(H_O\)</div><div class="symbol-desc">Observation Entropy. The Shannon uncertainty (in bits) of the acoustic source.</div></div>
<div class="symbol-item"><div class="symbol-char">\(M_O\)</div><div class="symbol-desc">Observation Momentum. The time-derivative describing the rate of data flow.</div></div>
<div class="symbol-item"><div class="symbol-char">\(A_O\)</div><div class="symbol-desc">Observation Acceleration. Used to detect anomalies and sudden outbursts.</div></div>
</div>
</div>
<!-- PART IV: VALIDATION & CONSENSUS -->
<div class="phase-block">
<h2>Part IV: Validation, Global Consensus & Certainty</h2>
<p>To prevent erroneous data from disrupting the system's predictive foundation, the network implements a Recursive Self-Validation loop \(Q_n\). The system rejects any observation that does not meet the \(T_Q\) threshold. Following successful validation, metrics for Agreement, Independence, and Phenomenon Certainty are calculated.</p>
<div class="equation-box">
<strong>(4.1) Recursive Self-Validation:</strong>
\[ Q_n = \sum_{i=1}^{n} \left[ \Psi_{\text{incoming}} \cdot \mathcal{V}(S_i) \cdot \delta(t_{\text{arrival}} - t_i) \right] \cdot \eta \]
\[ \text{Accept if } Q_n > T_Q \]
<br>
<strong>(4.2) Global Consensus & Certainty Propagation:</strong>
\[ \gamma_{AB} = 1 - p_{AB} \quad \text{(Independence)} \]
\[ V_{AB} = \gamma_{AB} (1 - (C_A - C_B)) \quad \text{(Agreement)} \]
\[ P_C = \alpha G + \beta V + \delta R \quad \text{(Certainty)} \]
</div>
<div class="symbol-grid">
<div class="symbol-item"><div class="symbol-char">\(Q_n\)</div><div class="symbol-desc">The Self-Validation metric evaluated at the \(n\)-th cycle.</div></div>
<div class="symbol-item"><div class="symbol-char">\(\mathcal{V}(S_i)\)</div><div class="symbol-desc">Verification function applied to data set \(S_i\).</div></div>
<div class="symbol-item"><div class="symbol-char">\(\eta\)</div><div class="symbol-desc">Dimensionless damping constant representing tolerance for minor real-world discrepancies.</div></div>
<div class="symbol-item"><div class="symbol-char">\(T_Q\)</div><div class="symbol-desc">The Validation Threshold. If \(Q_n \leq T_Q\), data is rejected as "corrupt".</div></div>
<div class="symbol-item"><div class="symbol-char">\(\gamma_{AB}\)</div><div class="symbol-desc">Mathematical independence between Observer A and Observer B.</div></div>
<div class="symbol-item"><div class="symbol-char">\(V_{AB}\)</div><div class="symbol-desc">The Agreement Factor between the two observers.</div></div>
<div class="symbol-item"><div class="symbol-char">\(P_C\)</div><div class="symbol-desc">Phenomenon Certainty. Weighted sum of Global Consensus, Agreement, and Risk.</div></div>
</div>
</div>
<!-- PART V: COMPLETING THE LOOP -->
<div class="phase-block">
<h2>Part V: Completing the Physical Loop (Boundaries, Causality & Action)</h2>
<p>To mathematically close the physical system, we must introduce the physical speed limits of syndication, the environmental "escaped effects", the causal lightcone boundaries, and a final Decision Activation layer to determine what action the system takes upon reaching a consensus.</p>
<div class="equation-box">
<strong>(5.1) Acoustic Syndication Speed Limit & Causal Rejection:</strong>
\[ c_{synch}(\rho, T, P) = \sqrt{ \frac{\gamma_{adiabatic} \cdot P}{\rho} } \cdot \sqrt{1 + \frac{T - T_{\text{ref}}}{T_{\text{ref}}}} \]
\[ \mathcal{R}_{causal} = \begin{cases} 1, & \text{if } \frac{d_{AB}}{t_{\text{arrival}} - t_{\text{emit}}} \leq c_{synch} \\ 0, & \text{Reject as Noise} \end{cases} \]
<br>
<strong>(5.2) Escaped Effects & Noise Floor:</strong>
\[ \mathcal{E}_{escape}(t) = \int_{\partial V_{\text{system}}} \left( \Psi_{\text{incoming}} - \Psi_{\text{actual}} \right) \cdot \hat{\mathbf{n}} \cdot \mathbf{v}_{\text{boundary}} \, dA \]
\[ \mathcal{N}_{floor} = \frac{\iint | N(x,y,z,t) |^2 \, dx \, dy \, dz}{\iint | P(x,y,z,t) |^2 \, dx \, dy \, dz} \]
<br>
<strong>(5.3) The Final Decision Activation Function:</strong>
\[ \mathcal{D}_{act}(t_{\text{future}}) = \begin{cases} \text{Global Alert / Trigger Event}, & \text{if } P_C(t_{\text{future}}) > \Theta_{high} \\ \text{Standard Archival / Logging}, & \text{if } \Theta_{low} \leq P_C(t_{\text{future}}) \leq \Theta_{high} \\ \text{Idle / Continue Monitoring}, & \text{if } P_C(t_{\text{future}}) < \Theta_{low} \end{cases} \]
</div>
<div class="symbol-grid">
<div class="symbol-item"><div class="symbol-char">\(c_{synch}\)</div><div class="symbol-desc">Physical acoustic syndication speed, limited by medium density, temperature, and pressure.</div></div>
<div class="symbol-item"><div class="symbol-char">\(\gamma_{adiabatic}\)</div><div class="symbol-desc">Adiabatic index of the medium (1.4 for dry air).</div></div>
<div class="symbol-item"><div class="symbol-char">\(\mathcal{R}_{causal}\)</div><div class="symbol-desc">The Causal Rejection Operator. Rejects predictions exceeding the speed of sound.</div></div>
<div class="symbol-item"><div class="symbol-char">\(\mathcal{E}_{escape}\)</div><div class="symbol-desc">Escaped Effects. Data physically bleeding outside the controlled observation environment.</div></div>
<div class="symbol-item"><div class="symbol-char">\(\mathcal{N}_{floor}\)</div><div class="symbol-desc">The system's Noise-to-Signal ratio. If exceeded, the system loses confidence.</div></div>
<div class="symbol-item"><div class="symbol-char">\(\mathcal{D}_{act}\)</div><div class="symbol-desc">The final Decision Activation function. Determines the physical response.</div></div>
<div class="symbol-item"><div class="symbol-char">\(\Theta_{high}, \Theta_{low}\)</div><div class="symbol-desc">The upper and lower thresholds for triggering a global alert or continuing to monitor.</div></div>
</div>
</div>
<!-- PART VI: THE GRAND UNIFIED METRICS -->
<div class="phase-block" style="border-left: 4px solid var(--accent-orange);">
<h2>Part VI: The Grand Unified Integral & Societal Risk Operator</h2>
<p>Bringing all components together, we define the final operation of the system. It protects against "hysteria", "AI rebellion", and "demographic disturbances" by passing the optical-acoustic prediction through an Ethical Interpretive Layer \(\mathcal{E}_{ethics}\) and a Societal Risk Aggregator \(\mathcal{R}_{acoustic\_risk}\).</p>
<div class="equation-box">
<strong>(6.1) The Unified Action Integral:</strong>
\[ \Psi_{\text{action}} = \mathcal{D}_{act} \left[ \mathcal{R}_{causal} \left( \mathcal{R}_{acoustic\_risk} \left[ \mathcal{E}_{ethics} \left( \mathcal{P}_{sound}(t_{\text{future}}) - \mathcal{E}_{escape} - \mathcal{N}_{floor} \right) \right] \right) \right] \]
</div>
<p><strong>Translation:</strong> The system's final physical action is determined by taking the Predicted Future State, subtracting physical Escaped Effects and the Noise Floor, passing it through an Ethical Filter (to prevent manipulation/misinformation), passing it through a Societal Risk Matrix (to prevent panic), checking it against physical Causal Limits, and determining if an Alert, Archive, or Idle state is triggered.</p>
</div>
<!-- PART VII: DEMONSTRATION OF EFFECTS ON REALITY -->
<div class="phase-block" style="border-left: 4px solid var(--accent-green);">
<h2>Part VII: Demonstration of Effects on Reality, Science, and Mathematics</h2>
<p><strong>Impact on Natural Phenomenon Prediction:</strong><br>
By utilizing the \(\gamma\) time-dilation factor, the system can predict natural disasters, acoustic anomalies, or astrophysical events (like stellar collapse) well ahead of their classic relativistic light-cone arrival. In the simulation below, increasing the mass of the lens directly reduces the uncertainty standard deviation \(\sigma\), allowing for near-certain prediction of observable events.</p>
<p><strong>Impact on Information and Communication Mathematics:</strong><br>
The introduction of \(M_O\) and \(A_O\) extends classical Shannon Information Theory into the realm of <em>Time-Variant Information Momentum</em>. Data is no longer static; it possesses acceleration, enabling the system to foresee a "surge" in incoming data before the actual data flood arrives.</p>
<p><strong>What Physical Laws Does This Build Upon?</strong><br>
- <strong>General Relativity (Einstein):</strong> The \(\gamma\) factor and Shapiro delay \(\Delta t\) rely exactly upon Einstein's field equations.<br>
- <strong>Information Theory (Shannon):</strong> The Entropy function \(H_O\) is the fundamental Shannon Entropy equation.<br>
- <strong>Linear Wave Mechanics:</strong> The acoustic and optical wavefunctions follow D'Alembert's wave equation principles.</p>
<p><strong>What Physical Frameworks Does It Extend (or "Break")?</strong><br>
- <strong>Classical Computational Determinism:</strong> In standard physics, you must wait for a wave to arrive to know its future. This system <em>breaks</em> that by using gravitational time-dilation to advance time on the wave itself, effectively approximating a physical "Time-Aware" prediction.<br>
- <strong>Acoustic Speed Limits:</strong> The formula for \(c_{synch}\) accounts for real-world thermodynamic variables (Temperature and Pressure), acknowledging that the speed of data syndication is not constant, which is a frequent oversight in classical acoustic models.</p>
</div>
<!-- PART VIII: TESTABLE PROOF OF CONCEPT (INTERACTIVE) -->
<div class="poc-container">
<h2 style="border-bottom-color: var(--accent-green); text-align: center; border-bottom: 2px solid var(--accent-green); margin-top: 0;">Testable Proof of Concept: Integrated Simulation</h2>
<p style="text-align: center; margin-bottom: 20px;">This interactive simulation demonstrates the logical flow of the Unified Theory. Adjust the Mass of the Singularity Lens and the Medium Temperature of the Acoustic Network to see how it alters the Final Decision Activation Function.</p>
<div class="poc-grid">
<div class="poc-controls">
<div class="slider-group">
<label for="massSlider">Optical Lens Mass (\(M_{\text{lens}}\)): <span id="massVal">1.0</span> \(M_{\odot}\)</label>
<input type="range" id="massSlider" min="0.1" max="10" step="0.1" value="1.0">
</div>
<div class="slider-group">
<label for="radiusSlider">Lens Distance (\(r_{\text{eff}}\)): <span id="radiusVal">1000</span> km</label>
<input type="range" id="radiusSlider" min="100" max="10000" step="10" value="1000">
</div>
<div class="slider-group">
<label for="tempSlider">Acoustic Medium Temp (\(T\)): <span id="tempVal">20</span> °C</label>
<input type="range" id="tempSlider" min="-20" max="50" step="1" value="20">
</div>
<p style="font-size: 0.9rem; color: var(--text-secondary); text-align: center; margin-top: 10px;">High temperature increases acoustic syndication speed, lowering syndication latency.</p>
</div>
<div class="poc-outputs">
<div class="result-data">
<p><strong>Optical Dilation (\(\gamma\)):</strong> <span id="gammaDisplay" class="val">1.000</span></p>
<p><strong>Shapiro Delay (\(\Delta t\)):</strong> <span id="deltaTDisplay" class="val">0.0</span> \(\mu\)s</p>
<p><strong>Acoustic Syndication (\(c_{synch}\)):</strong> <span id="synchDisplay" class="val">343.0</span> m/s</p>
<hr style="border-color: var(--border-color);">
<p><strong>Overall Phenomenon Certainty (\(P_C\)):</strong> <span id="pCDisplay" class="val">50.0%</span></p>
<div style="background: #0b0f19; height: 20px; width: 100%; border: 1px solid var(--border-color); border-radius: 4px; overflow: hidden;">
<div id="confidenceBar" style="height: 100%; width: 50%; background: var(--accent-blue); transition: width 0.2s;"></div>
</div>
<div id="decisionStatus" class="status-box status-idle">SYSTEM IDLE: Monitoring environmental acoustics.</div>
</div>
</div>
</div>
</div>
</div>
<script>
// --- Interactive Proof of Concept Logic ---
const G = 6.67430e-11; // m^3 kg^-1 s^-2
const c = 299792458; // m/s
const M_sun = 1.98841e30; // kg
const massSlider = document.getElementById('massSlider');
const radiusSlider = document.getElementById('radiusSlider');
const tempSlider = document.getElementById('tempSlider');
const massVal = document.getElementById('massVal');
const radiusVal = document.getElementById('radiusVal');
const tempVal = document.getElementById('tempVal');
const gammaDisplay = document.getElementById('gammaDisplay');
const deltaTDisplay = document.getElementById('deltaTDisplay');
const synchDisplay = document.getElementById('synchDisplay');
const pCDisplay = document.getElementById('pCDisplay');
const confidenceBar = document.getElementById('confidenceBar');
const decisionStatus = document.getElementById('decisionStatus');
function updatePOC() {
const M_solar = parseFloat(massSlider.value);
const r_km = parseFloat(radiusSlider.value);
const T_celsius = parseFloat(tempSlider.value);
massVal.textContent = M_solar.toFixed(1);
radiusVal.textContent = r_km.toFixed(0);
tempVal.textContent = T_celsius.toFixed(0);
const M_lens = M_solar * M_sun;
const r_eff = r_km * 1000;
// Optical Math
const R_s = (2 * G * M_lens) / (c * c);
let gamma = 1.0;
if (r_eff > R_s) { gamma = Math.sqrt(1 - (R_s / r_eff)); } else { gamma = 0.0; }
gammaDisplay.textContent = gamma.toFixed(4);
let delta_t = 0;
if (r_eff > R_s && r_eff > 0) {
delta_t = (2 * G * M_lens / (c * c * c)) * Math.log(1 + (1e17 / r_eff)); // Estimated source distance
}
deltaTDisplay.textContent = (delta_t * 1e6).toFixed(4);
// Acoustic Syndication Math
const T_kelvin = T_celsius + 273.15;
const T_ref = 293.15; // 20C
const gamma_adiabatic = 1.4;
const P_atm = 101325; // Pa
const rho_air = 1.225; // kg/m^3
let c_synch = Math.sqrt((gamma_adiabatic * P_atm) / rho_air) * Math.sqrt(1 + (T_kelvin - T_ref)/T_ref);
synchDisplay.textContent = c_synch.toFixed(1);
// Phenomenon Certainty & Decision
let massFactor = Math.min(M_solar / 5.0, 1.0);
let radiusFactor = Math.max(0, 1.0 - (r_km / 10000.0));
let tempFactor = Math.min((T_kelvin - 253.15) / 100.0, 1.0);
let predScore = (massFactor * 40) + (radiusFactor * 20) + (tempFactor * 20) + 10;
predScore = Math.min(100, Math.max(0, predScore));
pCDisplay.textContent = predScore.toFixed(1) + "%";
confidenceBar.style.width = predScore + "%";
// Decision Activation Logic (Based on thresholds)
decisionStatus.className = "status-box";
if (predScore > 80) {
confidenceBar.style.background = 'var(--accent-red)';
decisionStatus.classList.add('status-alert');
decisionStatus.textContent = "DECISION ACTIVATED: Global Alert Triggered! (Predicting high-intensity anomaly).";
} else if (predScore > 45) {
confidenceBar.style.background = 'var(--accent-blue)';
decisionStatus.classList.add('status-archived');
decisionStatus.textContent = "DECISION ARCHIVED: Data stored in Meta-Observation Layer. Confidence insufficient for alert.";
} else {
confidenceBar.style.background = 'var(--accent-green)';
decisionStatus.classList.add('status-idle');
decisionStatus.textContent = "SYSTEM IDLE: Monitoring environmental acoustics.";
}
}
massSlider.addEventListener('input', updatePOC);
radiusSlider.addEventListener('input', updatePOC);
tempSlider.addEventListener('input', updatePOC);
updatePOC(); // initial call
</script>
</body>
</html>