Monitoring change in a dynamic environment: spatiotemporal modelling of calibrated data from different types of fisheries surveys of Pacific halibut
Bibliographic record
Abstract
Monitoring distributional shifts in Arctic and subarctic fish species as environmental conditions change can be difficult due to sparse or infrequent surveys. Pacific halibut (Hippoglossus stenolepis) are found as far north as the Bering Strait, and future changes in sea temperatures and prey distribution may lead to an expanded range. For this and other species, it is therefore important to use as much survey data as is available when estimating density indices and other quantities of interest. Setline and trawl surveys in the eastern Bering Sea provide partial coverage each year, but the two gear types capture different size distributions of fish. We apply a calibration method to data from the setline and trawl surveys to produce consistent, spatially indexed estimates of indices of local density. The resulting estimates are then combined through spatiotemporal models that can incorporate environmental covariates to provide reliable density indices and to map the dynamic distribution of Pacific halibut. Such approaches may become increasingly important as climate change affects species distribution relative to historical survey footprints, and scientists must adapt to the use of new and variable data sources.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".