Spatially varying catchability for integrating research survey data with other data sources: case studies involving observer samples, industry-cooperative surveys, and predators as samplers
Bibliographic record
Abstract
Spatio-temporal models are widely applied to standardise research survey data and are increasingly used to generate density maps and indices from other data sources. We developed a spatio-temporal modelling framework that integrates research survey data (treated as a “reference dataset”) and other data sources (“non-reference datasets”) while estimating spatially varying catchability for the non-reference datasets. We demonstrated it using two case studies. The first involved bottom trawl survey and observer data for spiny dogfish ( Squalus acanthias) on the Chatham Rise, New Zealand. The second involved cod predators as samplers of juvenile snow crab ( Chionoecetes opilio) abundance, integrated with industry-cooperative surveys and a bottom trawl research survey in the eastern Bering Sea. Our integrated models leveraged the strengths of individual data sources (the quality of the reference dataset and the quantity of non-reference data), while downweighting the influence of the non-reference datasets via the estimated spatially varying catchabilities. They allowed for the generation of annual density maps for a longer time-period and for the provision of one single index rather than multiple indices each covering a shorter time-period.
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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 | 0.047 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| 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".