A probabilistic foundation for the study of larval fish feeding, growth, and mortality rates
Bibliographic record
Abstract
Survival through the larval phase is predicated on the probabilities of successful feeding, which dictates growth rates, and the probabilities of encountering predators. Here I perform a synthesis of feeding, growth, and mortality rates estimated during several studies from coastal Newfoundland, Canada, to provide a description of the probability distribution that can serve as a foundation of the expected distribution of vital rates. The standardized observations clearly follow that of skewed distributions, appropriately fit to a probability gamma distribution, with feeding demonstrating a stronger degree of skewness than either mortality or growth, possibly because each vital rate integrates prey–predator interactions over different time scales. Commonality in the underlying form of the distribution of vital rates in larval fish, along with clear functional relationships between gamma parameters, represents a probabilistic basis of expectations against which observations from prior or new studies can be contrasted. An example of the use of such expectations demonstrates that they can provide useful contextual information about the contrast among observations and our ability to identify their relationship with environmental drivers.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".