Summering on the bank: Seasonal distribution and abundance of monkfish on Georges Bank
Bibliographic record
Abstract
The American monkfish is an important commercial species that is widely distributed across a range of depths and temperatures from North Carolina to southern Nova Scotia, including on Georges Bank. We examined changes in the seasonal distribution and relative abundance of monkfish in the scallop access areas in Closed Area I and Closed Area II on Georges Bank using catch data from a three-year seasonal scallop dredge survey. Over the course of the survey, more than 6,000 monkfish were caught and measured, and clear seasonal changes in monkfish abundance were documented. Monkfish catch peaked in the summer and early fall when they were caught across the entire survey area, while they were caught only in deeper waters at the edges of the bank in the winter. Monkfish relative abundance was modeled using a generalized linear mixed model with a Tweedie distribution, and the final model, with month, depth, and bottom temperature as fixed effects, effectively explained the seasonal shifts in the location and relative abundance of monkfish observed during this study. The results suggest that monkfish movements are driven by seasonal changes in bottom temperature. Management measures for monkfish are determined primarily based on data collected during the Northeast Fisheries Science Center bottom trawl surveys, yet this survey catches few monkfish, adding uncertainty to stock assessments. Our research indicates that increasing the use of dredge surveys to collect data on monkfish would be a positive step toward improving monkfish assessments. If monkfish movements are impacted by changes in thermal habitat, their distributions may shift in response to climate change, increasing the need for improved monkfish assessment strategies to effectively manage the species in the future.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".