## 3 Results
### Energy
Surprising elevation appears in TM: KNN records 48.31, marginally exceeding the upper-tier ceiling. That idiosyncrasy illustrates locality-based classification occasionally resolving edge cases overlooked by parametric counterparts. Nevertheless, FM within KNN reaches 46.92, the highest inside the complete table, thereby eroding precision. UM minimum emerges not within the upper echelon but rather within Gradient_Boost at 0.96, again questioning any simplistic performance dichotomy. PM values remain muted, seldom breaching 3.10, indicating limited robustness under metric permutation. PC spectrum widens considerably: RBM escalates to 99.49, whereas GaussianNB restricts itself to 14.38. RBM relies upon prolonged Gibbs sampling that exercises every processing cycle; GaussianNB merely calculates closed-form density ratios, hence negligible load. FLOPS inspection reveals RBM as the least computationally intensive across all entries, consuming \(2.41\times 10^{14}\), nearly one order beneath tree ensembles. That outcome corroborates PC extremity: despite saturating CPU cycles, each Gibbs step contains elementary arithmetic; thus, throughput remains modest. Contrast that with KNN, where neighbor distance evaluation fosters \(1.97\times 10^{15}\), nearly parity with boosting algorithms, even though predictive value trails. The mismatch implies inefficiency, predisposition to high latency during inference pipelines without accuracy reimbursement. Juxtaposing both echelons uncovers trade-off vectors. Algorithms featuring discriminant analysis deliver exalted correctness at the cost of elevated PC; instance-based methods mirror correctness yet inflate FM; naive Bayes structures minimize PC but surrender TM variance. Energy-aware deployment within edge telescopic nodes and, therefore, might favor GaussianNB when deterministic output suffices, whereas ground-station clusters could absorb LIGHT_GBM overhead to maximise retrieval yield. As shown in Table 7, we report a mean snapshot of key hardware-utilisation metrics--free and used RAM, RAM and CPU load, average FLOPS, and the mean CPU/GPU energy draw--for every evaluated model. MH values suggest no linear interaction linking arithmetic intensity with predictive veracity. Scatter within FLOPS vs TM plane illustrates multiple Pareto front faces rather than a single contour. That multi-face profile mandates algorithm selection via multi-objective optimization, weighting TM, FM, PC differently according to mission doctrine. Carbon-aware protocols may pick permutations lying upon a minimal PC isocline; photometric survey campaigns seeking absolute recall may traverse toward maximal TM linear notwithstanding a larger MH footprint. Finally, terminological precision: polar consumption (PC) arises from instantaneous processor occupancy time-series averaged across complete epoch schedule; permutable metric (PM) equals standard deviation of MH values measured after synthetic permutation of label indices; undefined metric (UM) collects zero-division events inside confusion matrix computations. Such definitions guarantee replicability. Temporal dispersion of MH components further differentiates algorithms. Coefficient of variation (CV) of TM across ten folds registers 0.041 for QDA, 0.038 for RDA, whereas it is 0.121 for KNN. Elevated CV within KNN signifies unstable boundary decisions caused by sampling idiosyncrasy. PC also fluctuates: RDA PC standard deviation equals 4.7, whereas RBM PC standard deviation remains negligible given saturation near ceiling. Stability metrics, therefore, advocate discriminant analysis whenever repeatability prevails; cost, nevertheless, escalates. A synthetic performance index, designated Balanced Efficiency Quotient (BEQ) defined as \((TM-FM)/PC\), ranks MultiSURF foremost with value 0.071, while RBM burrows bottom with \(-0.021\). BEQ accentuates cases where modest resource expense accompanies a high correctness margin.