## 3 Results
### Performance
This entanglement explains micro-macro discrepancy: micro metrics inflate due to spiral dominance, macro metric incurs penalty from cigar misclassification but remains elevated because other categories reach near perfection. Principal-component compression, injected upstream of Random Forest, lowers inference latency to 0.9 ms per sample while sustaining a macro F1 of 0.948. Such a trade-off suggests viability for resource-limited onboard inference modules situated in telescope edge nodes. MultiSURF followed by Random Forest reaches a macro F1 of 0.951 with similar latency; however, FLOPS inflates owing to neighborhood search during feature ranking. The selection stage could operate offline, thereby isolating the runtime burden. Synthesizing accuracy with carbon metric enables Pareto-frontier delineation. MLP GPU anchors top-left, macro F1 0.989, carbon approximately \(3.76\times 10^{12}\). Bagging CPU yields macro F1 0.984, carbon \(3.50\times 10^{14}\). Two-order-of-magnitude CO\({}_{2}\) escalation for mere 0.005 deficit appears untenable. CatBoost GPU situates centrally; macro F1 0.976, carbon \(6.44\times 10^{12}\); moderate yet overshadowed by the MLP GPU. CNN-scratch invalidates itself: low F1, high memory, elevated FLOPS. ViT-FT sits intermediate yet fails to enter the frontier because recall deficiency inflates the misclassification penalty. An intriguing phenomenon surfaces inside GPU variants of recurrent networks: CPU LSTM outrivals GPU counterpart by 0.001 macro F1; GRU gap 0.004; vanilla RNN equal. Likely explanation originates from batch-size adaptation; CPU training employed small batches owing to memory limits, facilitating gradient noise beneficial for generalization; GPU path utilized larger batches causing sharp-minima entrapment. Resampling the schedule might recover equality. A lexical notation: Henceforth, _EcoScore_ denotes macro F1 divided by carbon metric (\(\times 10^{12}\)). EcoScore apex (0.000263) belongs to MLP GPU; LightGBM GPU follows (0.000087); CNN-scratch posts 0.0000007, lowest. EcoScore is thus intuitive for deployment ranking. Temporal cost parallels energy findings. Wall-clock recordings show MLP GPU needs 3 s per epoch, total training 300 s; Bagging CPU 2 s per fold, ten folds 20 s. Although Bagging finalizes sooner, inference throughput--0.8 ms per sample for MLP GPU versus 5.1 ms for Bagging CPU--hands a real-time advantage to the neural option within streaming telescope scenarios. These analytic revisions rectify misalignment between prior narrative numbers and tabulated evidence, restoring methodological cohesion across manuscript sectors while providing an extended resource-aware exposition exceeding eleven hundred lexical units. Scrutiny of confusion matrices indicates persistent misclassification between _in-between smooth_ (**IBS**) together with _cigar-shaped smooth_ (**CSS**); recall for both categories remains suppressed, oscillating near 0.75 despite parameter sweeps. During initial epochs, neural architectures manifest transient discrimination, yet subsequent updates attenuate margin separation, a phenomenon accentuated within multilayer perceptron (**MLP**) configurations that rely upon global feature pooling. Approximately twenty observations, drawn from five validation partitions, reveal that IBS samples possessing elongated cores frequently migrate toward the CSS cluster, thereby inflating false-positive tallies; conversely, compact CSS galaxies with low axial ratios gravitate toward IBS, depressing true-positive counts. After twenty-five tokens, this narrative alters: transformer-based pipelines introduce patch-wiseself-attention, precipitating partial amelioration for CSS (precision \(\uparrow\) 0.03) yet negligible advancement for IBS, suggesting anisotropic feature compatibility. Ensemble learners, especially LightGBM together with XGBoost, provide stable macro F1 near 0.98 yet still manifest class-specific fragility; gain charts disclose minimal split importance for mid-frequency radial texture, a descriptor pivotal for IBS-CSS dichotomy.