Modelling changes in the length–frequency distributions of fish larvae using field estimates of predator abundance and size distributions
Bibliographic record
Abstract
The goal of this study is to determine if an individual‐based size‐dependent model can realistically simulate changes in the length–frequency distributions of several species of fish larvae collected in Conception Bay in 1993 and 1994, using field estimations of growth and predator abundance. We first model the length–frequency distribution of field samples with the best possible estimates of mean growth rate. Then, we add predation mortality given the characteristics of the predator community observed during our surveys, which was composed of macrozooplankton and adult capelin. The larval fish community is generally not affected by predation by macrozooplankton, as the average instantaneous mortality rate predicted by the model was 0.004 day –1 . Fish larvae appear to be more vulnerable to predation by the population of adult capelin. We estimate that an abundance of adult capelin ranging between 0.2 and 1.0 individuals per 1000 m –3 may have a substantial impact on the larval fish community. The predictions of an individual‐based model are directly related to the accuracy of estimates of the mean growth rates of the larval fish cohorts. We find that it is difficult to differentiate size‐selective removal of individuals from random selection by analysing changes of the length–frequency distributions of the larval fish community.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".