Gastropod shell repair tracks predator abundance
Bibliographic record
Abstract
Abstract Predator‐prey interactions can shape biological communities and drive evolutionary change. The assessment of predation rates in modern and ancient ecosystems presents many challenges. We examined the utility of one common predation metric, shell repair frequency, as a proxy for crushing predation on marine gastropod populations. Our six localities near Bamfield, British Columbia, represented two contrasting predation regimes: three high‐wave‐energy (exposed) environments have low predation rates, while three low‐wave‐energy (protected) environments exhibit high predation rates. We measured the occurrence of repair scars for local populations of the gastropod Chlorostoma funebrale, and recorded multiple independent variables (shell measurements, water velocity, predatory crab abundance) at each locality. Fisher's exact test established that the repair frequencies observed in the protected localities were significantly greater than those of the exposed environments. Factors important in predicting the likelihood of an individual having a repair scar were examined using binomial logistic regression. A model containing the main effects of crab abundance, shell height, shell thickness and water velocity provided the best fit in predicting the presence of repair scars, with crab abundance having the largest contribution. The strong relationship between the presence of repair scars and predator abundance indicates that repair frequency is a valid tool for assessing predation intensity in gastropod populations.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".