Benthic Habitats and the Effects of Fishing
Bibliographic record
Abstract
Multibeam sea floor mapping technologies have provided the capability to accurately, and cost effectively, image large areas of the seabed. Imagery provides base maps of sea floor topography from which targeted surveys can be planned to map sea floor sediments and associated benthic communities. Over the last five years extensive multi-disciplinary surveys have been carried out on Browns, German and Georges Banks. The government of Canada entered into a partnership with the scallop industry to map bathymetry, surficial sediments and benthic communities. The new knowledge has been used by industry, and has implications for fisheries management. Associations between substrate type and benthic community composition have enabled precise maps of scallop habitat to be produced and links between scallop abundance and substrate to be established. The environmental and economic benefits have been immediate, with reduced effort to catch set quota, less bottom disturbance, and containment of fishing activity to known scallop grounds. Stock assessments and management practices are improved. Other pilot projects in Atlantic Canada and the northeastern USA have demonstrated the value of integrated sea floor mapping in designating marine protected areas (The Gully, Stellwagen Bank), in identifying offshore hazards such as landslides, in siting offshore structures, cables and pipelines, and in addressing environmental issues such as the routing of outfalls and disposal of dredge materials. In recognition of the power of these new tools and digital map products, Canada is considering development of a national mapping strategy to provide the foundation for sustainable ocean management in the 21st century.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".