Estimating larval fish growth under size-dependent mortality: a numerical analysis of bias
Bibliographic record
Abstract
The early larval phase is characterized by high growth and mortality rates. Estimates of growth from both population (cross-sectional) and individual (longitudinal) data may be biased when mortality is size-dependent. Here, we use a simple individual-based model to assess the range of bias in estimates of growth under various size-dependent patterns of growth and mortality rates. A series of simulations indicate that size distribution of individuals in the population may contribute significantly to bias in growth estimates, but that typical size-dependent growth patterns have minor effects. Growth rate estimates from longitudinal data (otolith readings) are closer to true values than estimates from cross-sectional data (population growth rates). The latter may produce bias in growth estimation of about 0.03 day–1 (in instantaneous, specific growth rate) or >40% difference in some situations. Four potential patterns of size-dependent mortality are tested and analyzed for their impact on growth estimates. The bias is shown to yield large differences in estimated cohort survival rates. High autocorrelation and variance in growth rates tend to increase growth estimates and bias, as well as recruitment success. We also found that autocorrelated growth patterns, reflecting environmental variance structure, had strong impact on recruitment success of a cohort.
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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 | 0.007 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".