Detecting somatic growth trends for summer flounder (<i>Paralichthys dentatus</i>) using a state-space approach
Bibliographic record
Abstract
In the past four decades, summer flounder (Paralichthys dentatus) abundance in the Northwest Atlantic shifted with no definitive explanation for this shift. Here, we extract patterns in population-level size variability from summer flounder mean length-at-age data from 1992 to 2015 using an autoregressive state-space modeling approach and annual fishing and oceanographic covariates. We found that summer flounder length-at-age varies annually, suggesting that productivity can vary annually due to variable sizes. We found that location and depth of the observed fish, exploitation, and the Gulf Stream appeared to influence the magnitude of length-at-age variation, whereby lengths-at-age were above the mean length at greater depth, northern latitudes, and during periods characterized by a northerly Gulf Stream position or higher fishing exploitation. These factors should be considered as indicators to track size and more accurately understand productivity as the summer flounder population changes and the fishery adapts in response. This study brings us closer to annual proxies for summer flounder length-at-age variation, an important tool for fisheries managers and stock-assessment scientists to more accurately predict fish stock abundances and productivity.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 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".