A novel approach for estimating growth and mortality of fish larvae
Bibliographic record
Abstract
Abstract Accurate estimates of growth and mortality are needed to understand drivers of production and cohort success. Existing methods for estimating mortality rates, such as catch-curves, require large sample sizes, as they work by grouping individuals into age-bins to determine a frequency distribution. Yet, sampling enough larvae is often not possible at fine scales within the constraints of research projects, due to low density of larvae in pelagic environments. Here, we develop a novel method to simultaneously estimate growth and mortality rates of fish larvae as a continuous function of size using theory of size-structured populations, eliminating the need to group data into age-bins. We compare the effectiveness of our model to existing methods by generating data from a known distribution. This comparison demonstrates that while all models recover correct parameter values under ideal circumstances, our new method performs better than existing methods when sample sizes are low. Additionally, our method can accommodate non-linear growth and mortality functions, while also allowing growth and mortality to vary as functions of environmental co-variates. This increased accuracy and flexibility of our method should improve our ability to relate variability in larval production to environmental fluctuations at finer spatial scales.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".