Modeling pulse dynamics of juvenile fish enables the short-term forecasting of population dynamics in Japanese pufferfish: a latent variable approach and hindcasting
Bibliographic record
Abstract
Short-term forecasting of population dynamics of fish stocks is required for scientific management recommendations such as catch quotas but is difficult to perform accurately because of the inevitable time lag between data collection and management implementation. Here, we developed a method using small-scale survey data for juvenile Japanese pufferfish ( Takifugu rubripes) to shorten this time lag and achieve accurate short-term forecasting. A survey of juvenile pufferfish at a local sandy beach provides data for the strength of year classes before fisheries recruitment; however, use of the raw data is difficult owing to the small sample size and large observation errors. We found that a random-effects model overcame these problems and more accurately predicted pulse patterns of catch rates to derive a standardized recruitment index than a fixed-effects model. A stock assessment model using the standardized recruitment index outperformed models without the standardized recruitment index with respect to hindcasting bias and prediction skill. This study highlights the applicability of a latent variable approach for standardizing small-scale survey data and thereby for unbiased forecasting of short-term fish dynamics.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".