Multivariate dissemination of species relationships for use in marine spatial planning
Bibliographic record
Abstract
Employing ecological approaches to fisheries management or comprehensive marine spatial planning requires that species assemblage structure be accounted for. Fish and invertebrate spatial distributions from the National Marine Fisheries Service demersal trawl survey conducted in the Georges Bank – Gulf of Maine region were analyzed by bootstrapped principal component analysis (PCABtsp) and normal PCA (PCANrml). PCABtsp produced confidence limits for eigenvalue stopping rules and for eigenvectors to identify significantly correlated species. Stopping rules identified the first six principal components (PCs) as relevant. Initially, summer and fall survey data from 1963 to 2006 were analyzed, but high eigenvector variation led to reductions in the species and time series used. Confidence interval variation was achieved through removal of highly migratory species and restriction of the time series. PC scores were mapped using inverse distance weighted interpolation to reveal multispecies spatial arrangements. Core areas of species groupings and overlapping zones of higher diversity can be delineated and, even under high fishing pressure with large compositional changes, the assemblages maintained robust spatial organization. This spatial organization could be employed to protect appropriate species groups and minimize bycatch. Careful analysis of survey data can help ensure area-based management schemes are consistent with ecological scales.
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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 | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 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".