Spatially integrated modelling of data-limited orange roughy (<i>Hoplostethus atlanticus</i>) using environmental covariates
Bibliographic record
Abstract
Spatial stock assessment models are recognised as increasingly important for estimation of stock status and a sustainable exploitation rate. The inclusion of movement between spatial units within a model is difficult because the data requirements are high. However, for populations with low levels of spatial exchange it is possible to reduce the data requirements by distributing information on biological parameters between neighbouring units, or units with shared environmental conditions. This can allow spatial modelling to be applied even in data-limited situations. We develop this approach here through application to orange roughy (Hoplostethus atlanticus) subpopulations inhabiting neighbouring seamounts in the South Pacific. Despite limited data for each seamount, we were able to simultaneously fit multiple, localised, process-based models of the depletion dynamics. This was achieved by sharing information on the unexploited population size via known environmental covariates, with the relationship estimated in a hierarchical and integrated manner during the model fit. Cross-validation demonstrated that this approach can compensate for a lack of seamount-specific abundance data and improve ability of the model to estimate localised depletions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".