Environmental drivers of golden tilefish (<i>Lopholatilus chamaeleonticeps</i>) commercial landings and catch‐per‐unit‐effort
Bibliographic record
Abstract
Abstract We explored a range of potential low and high‐frequency environmental drivers of fishery production (landings) and catch‐per‐unit‐effort (CPUE) for northern and southern stocks of golden tilefish (Lopholatilus chamaeleonticeps), a stenothermic species that prefers a narrow band of habitat along the continental shelf and upper slope of the eastern US. Random forest regression, a machine learning technique, was used to examine the impact of numerous and sometimes correlated environmental covariates. We used important random forest covariates to inform construction of a more parsimonious generalized additive mixed model for each data type and stock. We identified several potential environmental drivers of golden tilefish fishery and stock dynamics, including low‐frequency climate indices, oceanographic currents, and high‐frequency oceanographic conditions. Both Atlantic Multidecadal Oscillation (AMO) and North Atlantic Oscillation indices were associated with historical golden tilefish landings for the northern stock spanning 1915–2000 at lags of 7 and 3–4 years, respectively. CPUE for both stocks (north: 1995–2017, south: 1994–2018) was associated with the AMO and oceanographic currents. In addition, northern stock CPUE was negatively related to Labrador Current flow and positively related to northerly position of the Gulf Stream. Southern stock CPUE was associated with seasonal Florida Current transport, monthly sea surface temperatures, and latitude. Oceanographic currents and water temperature primarily influenced within‐year CPUE, indicating a potential effect on adult fish or fisher behavior. In contrast, low‐frequency climate indices were associated with CPUE and landings at lags of 3–7 years, indicating their primary impact was on recruitment strength.
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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 | 0.000 | 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".