Bibliographic record
Abstract
The rise of deep learning challenges the longstanding scientific ideal of insight—the human ability to understand phenomena by uncovering underlying mechanisms. From a physics perspective, we examine this tension through a case study: a physics-informed neural network (PINN) trained on rarefied gas dynamics governed by the Boltzmann equation. Despite strong physical constraints and a system with clear mechanistic structure, the trained network’s weight distributions remain close to Gaussian, showing no coarse-grained signature of the underlying physics. This result contrasts with theoretical expectations that such networks might retain structural features akin to discrete dynamical systems. We argue that high predictive accuracy does not imply interpretable internal representations and that explainability in physics-informed AI may not always be achievable—or necessary. These findings highlight a growing divergence between models that predict well and those that offer insight.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.010 | 0.044 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".