View from below: experimental manipulation of water surfaces to evaluate visual integrity of heron-like models through Snell’s window
Bibliographic record
Abstract
Abstract The water–air interface is a globally widespread habitat for interactions between prey and predators. We experimentally manipulated water surface conditions (flat, smooth waves, three levels of current-induced turbulence) and digitally quantified the visual integrity of above-surface models from a subsurface perspective. Progressive fragmentation was present in each of the models (upright heron, crouched heron, vertical block, horizontal block) with increased departure from flat surface conditions. Smooth directional waves produced multiple horizontal bands (shadows) that moved across the models while surface currents distorted the profile, including progressive disintegration of the models appearance into multiple fragments of different sizes. This fragmentation is caused by scattered surface irregularities interacting with waves and is accentuated at the broken periphery of the optical window, reducing recognition of the models. Unexpectedly, we found that bands and fragments emerging from different surface conditions resemble common frontal plumage patterns on some Ardeidae and shorebirds (Charadriiformes). While these natural plumages are widely recognized to reflect a diversity of adaptations, including camouflage in terrestrial habitats, we suggest that their resemblance to water surface-induced fragmentation might also reflect foraging adaptations of predators though the water–air interface.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".