Gully Marine Protected Area Monitoring : Fish and Fishery Resources
Bibliographic record
Abstract
Of four Indicators proposed in 2010 for monitoring the fish of the Gully MPA, only one that utilizes data from on-going halibut surveys has been implemented. Those data suggest that the ecosystems have been broadly stable since 1998, though subject to regional trends in some species – Atlantic Halibut itself perhaps increasing by about 5% per year. It is recommended that routine sampling continue on the one fixed station of the Halibut survey that falls within the MPA, while more attention be paid to setting the gear at a constant depth. Since 2015, regular Snow Crab trawl surveys have included ten fixed stations around the shallow margins of The Gully. To date, the resulting time series are too short for any conclusions to be drawn but emerging trends suggest that the data may have future value in MPA monitoring, if the surveys continue to work the ten stations. Closer control of the seasonal timing of the sampling there would be an advantage. In contrast, the existing data from stratified-random groundfish-trawl surveys, which have been on-going since 1970, have no value in MPA monitoring. Artifacts arising from the broad variety of depths sampled in different years obscure any temporal trends. Those data are nevertheless summarized here for their contribution to understanding of the biodiversity of The Gully. Lastly, midwater-trawl surveys during 2007–10 have generated data that could provide a quantitative baseline for future monitoring of the micronekton in the MPA but no further sampling has been attempted during the past decade.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".