Death from near and far: alternate perspectives on size-dependent mortality in larval fish
Bibliographic record
Abstract
This essay contrasts the inferences about the patterns of size-dependent mortality in larval fish based on the traditional catch-curve approach with that achieved through the vertical life table method in an application to data from coastal Newfoundland. Although both approaches reveal that the average mortality rates decline with increasing body size, the rate of decline estimated using the vertical life table approach is much less pronounced than estimated from the catch-curve method. More important, however, is that on a case-by-case assessment the vertical life table reveals that mortality increases with increasing body size in 70% of the cases and declines in the remainder. Instances with greater rates of loss in larger individuals are consistent with larvae becoming more susceptible to the dominant planktivore in the study region. The contrasting results indicate that the patterns of change in mortality rates need to be measured over relatively short-time and/or length intervals. Such inferences have important implications for the development of studies dealing with larval fish dynamics. To be effective and applicable, comparative analyses that aim to develop macroscopic principles for the early life stages of fish must take the local food web structure into consideration to gain appropriate understanding of the trophic interactions that most strongly affect losses from larval fish 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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".