Habitat mapping in support of improved assessment and management of scallop fisheries in the Maritimes region, Canada
Bibliographic record
Abstract
The importance of incorporating spatial information into the assessment and management of scallop populations has long been recognized. Due to the strong association between scallops and substrate type, scallop distributions can be relatively well represented by seafloor habitat maps. These maps, combined with geospatial fishery data, have tremendous potential to improve our understanding of the spatial patterns and complexities of scallop populations and their dynamics in response to fishing. Further, marine habitat maps provide spatial classification of patterns which can be used to understand ecosystem dynamics and support ecosystem-based fisheries management. In the Maritimes Region of Atlantic Canada, the majority of commercial scallop fishing areas (SFAs) have been surveyed using multibeam echosounders (MBES). In 2010, a detailed underwater imagery survey was conducted in SFA 29W off south-west Nova Scotia and these data were subsequently modelled with MBES bathymetry, backscatter, and associated metrics to derive both a species-specific habitat map for the sea scallop, Placopecten magellanicus, and a benthoscape map of broad bio-physical characteristics of the seafloor. These habitat maps have subsequently been used to improve the understanding of scallop population dynamics, develop a habitat based population model, set biological reference points for fisheries management that incorporates habitat associations, and evaluate overlap between the fishery and benthic communities; all of which demonstrates the tremendous potential of habitat mapping as a tool for improving fisheries science and management.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".