Application of habitat mapping to coastal aquaculture research: case studies from eastern Canada
Bibliographic record
Abstract
Management of marine finfish aquaculture requires high-quality spatial data to address interactions with local ecosystems, including aspects of both biological communities and ecosystem function. Understanding of the spatial distribution of effects is critical for the implementation of marine spatial planning (MSP), where human activities are spatially managed to ensure sustainable use of resources. Conceptual and technological advances in habitat mapping have greatly increased the availability of marine spatial data, facilitating advances in several areas of aquaculture research. Multiple case studies will be presented highlighting the use of spatial habitat data for aquaculture research, ranging from local to bay-scale investigations. Particular focus will be given to various methods of spatial data collection, including single-beam acoustics as well as optical data from satellites and UAVs. Mapping data is used alongside oceanographic data and model outputs to scale biogeochemical processes (e.g. benthic nitrogen cycling) in aquaculture areas. Substrate maps are also used to estimate habitat use around aquaculture sites by important wild species such as American lobster (Homarus americanus) and Atlantic salmon (Salmo salar). Spatial data also provide critical information for the management of fish health through epidemiological models of disease and pathogen transmission, crucial for ensuring sustainable aquaculture development in the marine environment. Implications for future MSP efforts in Nova Scotia and Eastern Canada will be discussed along with plans for future research activities in the aquaculture sector.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".