Rapid inference of larval fish recruitment potential from size spectrum models
Bibliographic record
Abstract
The ratio of mortality to growth of larval fish provides a metric of a cohort’s “recruitment potential”. Estimating recruitment potential is arduous, requiring growth and mortality to be estimated independently. Here, we propose using the exponent of size spectrum models to indicate recruitment potential from body measurement data alone. This approach has several advantages including (1) reducing data collection times, (2) removing uncertainty in estimates generated from age estimation, and (3) allowing re-analysis of larval fish databases or archived collections. To test the validity of this approach, we conduct simulations comparing estimates of recruitment potential from an abundance spectrum model with other common methods. By varying larval flux rates, growth, mortality, and measurement error, we show the abundance spectrum model is more accurate and precise at smaller sample sizes, and more robust to variance in individual rates and measurement error of ages, but more susceptible to measurement error of size. We highlight that a size-based approach to estimating recruitment potential provides another useful tool for understanding larval survival, reducing resource demands on research compared to traditional methods.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".