Layers Utilized by an ArcGIS Model to Approximate Commercial Coral and Sponge By-catch in the NAFORegulatory Area
Bibliographic record
Abstract
This report specifically addresses Fisheries Commission Request #16: Implement and/or further refine the existing GIS simulation/modelling framework, in conjunction with the VMS data supplied by the NAFO Secretariat ..., brought forth in the Fisheries Commission 33rd Annual Meeting Report (NAFO, 2011a). Data layers utilized by the model as well as their various means of construction are described in detail including the generation of NAFO VMS trawl lines. These VMS trawl line data were used to better understand fishing behaviour and also generate a new standard trawl length (13.8 nm) to be utilized by trawl simulations. The justification for utilizing just the Spain/EU research trawl by-catch dataset instead of the combined Canada/Spain/EU dataset for the production of higher resolution sponge and sea pen biomass surfaces is also made. It is demonstrated how this high resolution (5x5 km cell grid) Spain/EU data biomass layer could be utilized with 2000 randomly placed and oriented 13.8 nm simulation trawls to generate by-catch values, organized by thresholds, to capture the distributional extent of high concentration sponge and sea pen areas. This serves as the basis for a kernel density polygon analysis that calculates a commercial sponge and sea pen encounter threshold (Kenchington et al., 2011). Finally, using the Spain/EU only high resolution biomass surface, by-catch output from VMS trawls and their simulated 13.8 nm standard trawl line counterparts are compared.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".