## 3 Results
### Performance
Analytical scrutiny of Table 5, portraying average macro-averaged precision, recall, and F1 metrics across ten folds, elucidates a pronounced reduction in precision for CNN-VGG-16-TL and Vision Transformer (ViT) models compared to other evaluated architectures. For instance, CNN-VGG-16-TL yields notably diminished precision at approximately 65.5%, while ViT models record marginally superior but still inadequate precision levels around 75.1%. Such marked divergence necessitates a detailed explanatory interpretation. The relatively lower precision of CNN-VGG-16-TL primarily arises from its inherent methodological constraints associated with transfer learning (TL). Specifically, TL architectures rely on feature mappings initially calibrated upon general-purpose imagery (e.g., ImageNet), subsequently transferred to the astronomically oriented Galaxy Zoo dataset. Consequently, domain disparity introduces representational incongruities, where convolutional kernels optimally tuned for natural images become suboptimal when applied to the subtle structural intricacies endemic to galactic morphologies. Moreover, parameter freezing during TL significantly restricts adaptive flexibility, thereby suppressing precision by inserting feature-extraction rigidity. ViT's inferior precision similarly originates from architectural inadequacies relative to the nuanced astronomical domain. Although theoretically potent in capturing extensive spatial dependencies through patch-wise self-attention mechanisms, ViT's methodology necessitates copious data quantities to harness its representational potential fully. Conversely, the moderate scale and intricate morphological delineations characteristic of the Galaxy Zoo dataset inadequately fulfill these training schema prerequisites. Moreover, ViT's coarse-grained patch embedding schema precipitates a granularity deficiency, inserting representational losses precisely where refined spatial resolution is indispensable. Consequently, nuanced morphological characteristics essential for precise galactic classification remain inadequately encoded, directly diminishing classification precision. A second stratum encompasses discriminant analysis (RDA, QDA, and LDA) together with a penalized linear classification. Macro F1 spreads from 0.980 up to 0.983 (RDA, QDA). RDA secures maximal CPU utilisation (79%) and, as summarised in the hardware-energy snapshot of Table 6 and the utilisation details of Table 7, incurs a substantial carbon cost (see Table 8). Yet its macro F1 parity with LightGBM suggests diminishing returns for the extra resource outlay: covariance-inversion overhead outweighs the small accuracy gain. LDA positions itself marginally beneath, still surpassing 0.981, indicating that diagonally constrained covariance suffices within five-class galaxy morphology once features derive from ResNet embeddings. Naive-Bayes variants furnish a third stratum. Multinomial as well as Gaussian versions reach 0.981 macro F1, whereas Bernoulli underperforms (0.923). The inferential gulf derives from sparsity bias entrenched within Bernoulli likelihoods: galaxy embeddings are dense, so binarisation discards amplitude nuances necessary for inter-class separation, and conditional independence therefore fails to produce competitive decision boundaries. Deep neural assemblages represent the terminal stratum. Two sub-species surface. Recurrent constructs--LSTM, GRU, vanilla RNN--manifest contrasting arenas. LSTM secures 0.985 (CPU) together with 0.985 (GPU), thus matching Bagging, surpassing many ensembles. GRU rests at 0.984 (CPU), 0.980 (GPU), still near apex. Vanilla RNN presents 0.971 in both venues. The hierarchy resonates with gating sophistication; temporal gates mitigate vanishing gradients even though sequence length equals feature vector length rather than temporal index. Resource metrics confirm moderate FLOPS yet elevated memory allocations attributable to hidden-state replication across folds. Feed-forward Neural Net and CNN derivatives, Vision Transformer fine-tune (ViT-FT)--reveal wider dispersion.