## 3 Results
### Energy
The same algorithm compiled for CUDA realizes 9.60 \(\times\) 10\({}^{12}\) operations, three magnitude lower, while sustaining accuracy within 0.005 absolute--memory bandwidth saturation prevented heavier arithmetic; therefore, carbon plume shrank significantly. Such evidence underlines that FLOPS maxima neither guarantee a superior score nor necessarily inflate emissions; architectural alignment moderates both. Memory telemetry presents an auxiliary narrative. Convolutional Visual Geometry Group--transfer-learned--allocates 1.28 GB VRAM, though macro F1 collapses to 0.653; parameter redundancy in early layers aimed at natural imagery fails to transfer, leading to both underperformance and inflated EcoLoad. In contradistinction, Vision Transformer fine-tune occupies 0.19 GB, secures 0.941 macro F1, whereas EcoLoad settles at 27%, positioning the model within an efficiency basin attractive for edge telescopic stations where power budgets remain austere. Replacing the frozen patch encoder with a low-rank adapter could compress the footprint further without metric erosion, as preliminary ablation suggests. Anomalous inversion appears, too. MultiSURF consumes \(1.77\times 10^{15}\) FLOPS while utilizing merely 17.08% of processor capacity; vectorized neighborhood scans engage memory more than arithmetic, thus floating-point pipelines idle. Although classification subsequent to selection shares identical accuracy with tree ensembles, total EcoLoad remains lower, promoting offline adoption during dataset curation. Summative alignment between metrics and narrative requires reinterpretation of initial claims. Elevated carbon signatures originate not exclusively from operation count but from the convolution between utilization saturation, memory access pattern irregularity, and thermal throttling windows, along with kernel launch granularity. GPU paths diminish average load, scatter power into intermittent spikes, and therefore accumulate fewer kilograms of CO2 equivalent despite variable FLOPS. Scalar-bound algorithms capable of near-socket saturation, such as RBM, amplify emissions even when arithmetic density is modest. Consequently, targeted optimization ought to prioritize scheduler duty modulation, cache coherence improvement, batch size calibration, in place of sole pursuit of FLOPS minimization. Such strategies promise emission abatement while sustaining astronomical-image classification fidelity, fulfilling Green-AI desiderata without recourse to accuracy sacrifice. Figure 5 depicts computational resource consumption of the models executed on CPU in comparison to GPU resources. Resource usage is quantified through CPU load percentage, FLOPS, and memory utilization, visually represented via marker sizing. FLOPS values, indicative of computational throughput, are plotted on a logarithmic scale for clarity. Models executed on GPUs generally exhibit reduced CPU utilization but vary notably in computational throughput and memory demand. Figure 5: Comparison of computational resource consumption (CPU vs. GPU models). FLOPS denotes Floating-Point Operations per Second, represented on a logarithmic scale. Marker size correlates proportionally with memory usage. Colours denote model families—orange (CatBoost), red/magenta (XGBoost), and cyan/blue (MLP).