Stock Assessment of the Lemon Shark off the Southeast United States
Bibliographic record
Abstract
Abstract The Lemon Shark Negaprion brevirostris is a large coastal shark that commonly occurs in the shallow nearshore waters of the tropical western Atlantic Ocean. There are conservation concerns for this species due to fisheries exploitation, low productivity, anthropogenic disturbance at nursery sites, and the depleted status of other large coastal sharks, but no quantitative assessment of Lemon Shark stock status is available. We synthesized available data to develop a stock assessment of the Lemon Shark in the western North Atlantic. All information on stock identity was considered to define a fishery management unit off the southeastern United States. Stock abundance and trends in fishing mortality were estimated from 1981 to 2017 using a Bayesian state–space surplus production model. The model incorporated prior knowledge of Lemon Shark demography, catches, and a combination of 11 indices of abundance. Seven model configurations that fit the data well and produced plausible estimates were used to evaluate the sensitivity of posterior estimates to assumed priors and data decisions. Results suggested that Lemon Shark stock abundance has been relatively stable since the mid-1990s, with some estimates of prior depletion. Estimates of relative fishing mortality indicate earlier periods of overfishing, with a decrease in fishing mortality since the early 2000s. These estimates of population trends provide information for future fisheries management and conservation.
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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 | 0.000 |
| 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".