An improved method of microhabitat assessment relevant to predation risk
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Animals may select the microhabitats they use in response to a real or perceived risk of attack by visually hunting predators. However, to demonstrate this requires measuring visual cover at the microhabitat level, which can be labor‐intensive and may require specialized equipment. Simpler methods lack repeatability, particularly when multiple observers are involved. We devised, and describe here, the quadrant cover method (QCM), which provides rapid, objective assessment of the degree of concealment that microhabitats provide from visual predators. Our method gives results that correlate strongly with those obtained using a conventional sight board, but requires less than 25% of the time. The method is highly repeatable, with negligible observer bias. The QCM is ideal in microhabitat studies in which the variable of interest is visual exposure to other animals such as predators.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it