Reconstructing predation intensity on crinoids using longitudinal and cross-sectional approaches
Bibliographic record
Abstract
Predation has been hypothesized as important to crinoid ecology, and numerous crinoid traits have been linked to predation. However, testing such hypotheses requires some assessment of predation intensity, or pressure. Although direct observations of predatory activity on crinoids are exceedingly rare in the Recent, and unobservable in the fossil record, evidence of predation exists in the form of sublethal damage, especially to their arms. Substantial data exist on the relative frequency, or prevalence, of such injuries, but estimating predation intensity in taxa with ephemeral injuries, such as crinoids, requires combining the prevalence of injuries with rates at which they heal (regenerate). An alternate, independent estimate of predation intensity involves gathering longitudinal data on the number of injuries incurred by particular individuals over a given time span. In this study, predation intensity on crinoids is explored experimentally using these two approaches. We demonstrate that for the two feather star species examined, Capillaster multiradiatus and Clarkcomanthus mirabilis , both methods produce reasonably consistent results and that predation intensity is slightly lower on the latter perhaps because it responds to tactile stimulation by crawling deeper into its perch, whereas the former shows no response.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".