Behavior and recruitment success in fish larvae: variation with growth rate and the batch effect
Bibliographic record
Abstract
Predation-mortality risk for red drum (Sciaenops ocellatus) larvae does not appear to be related to their growth rate, but important differences in behavioral performance occur between batches of larvae. This conclusion is based upon field-enclosure and laboratory experiments that assessed the degree to which predation-mortality rates and behavioral survival skills vary with growth rate. In field enclosures, populations composed of 15 fast-growing larvae and 15 slow-growing larvae of a comparable size were exposed to a predatory fish. Growth rate did not affect predation rate. In the laboratory we measured 11 survival skills on 100 larvae of a common size from 10 batches of eggs. For each batch, behavioral performance of fast-growing larvae was compared with that of slow-growing larvae. Growth rate did not affect performance in 10 of the 11 survival skills, but behavioral performance varied among treatment groups (growth rate × batch), with higher performance in most survival skills for some treatment groups and consistently poorer performance for other groups. This coordinated pattern of behavioral performance forecasts differential survival among batches. The variation among batches may be related to timing of spawning within the reproductive season of this serially spawning species.
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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 | 0.004 |
| 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) | 0.001 | 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".