Characterizing fish populations: effects of sample size and population structure on the precision of demographic parameter estimates
Bibliographic record
Abstract
We examined precision of size, age, growth, and mortality parameters for four reef fishes at sample sizes ranging from 25 to 1000 using bootstrapped population samples. The results are illustrative rather than prescriptive in that we do not determine "optimum" sample sizes, but rather describe improvements in precision with increasing sample size. Furthermore, we do not address the related issue of accuracy. In general, a sample size needed to be tripled to halve precision at that sample size. Mean lengths and ages were most precise, reaching 10% by a sample size of 75 for all species. von Bertalanffy growth parameters were up to an order of magnitude more precise when constraints were placed upon the fitting process. Asymptotic lengths, L[Formula: see text], were up to eight times as precise as Brody growth coefficients, K. Catch curves were generally less precise than two other mortality estimators, but we cannot advocate any estimator until accuracy is addressed. We propose a general rule of collecting an average of 710 fish per age-class to estimate a variety of parameters. However, we more strongly suggest applying similar analyses for focal species and, where possible, with consideration of the application of parameters (e.g., sensitivity analyses).
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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 | 0.108 | 0.398 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".