Biophysical and ecological overview of a study area within the Labrador Inuit settlement area zone
Bibliographic record
Abstract
The Government of Canada has committed to protect 10% of coastal and marine areas by 2020, which requires the creation of new protected areas throughout Canada’s marine territory. The Labrador Inuit Land Claims Agreement (LILCA), signed in 2005, established the Labrador Inuit Settlement Area (LISA) which includes 72,520 km2 of lands and 48,690 km2 of coastal waters. In 2017, the Nunatsiavut Government signed a Statement of Intent with Environment and Climate Change Canada (ECCC) and Fisheries and Oceans Canada (DFO) to establish a marine plan for the Nunatsiavut Zone, including environmental protection. This report, and the associated Proceedings document, capture the results of a biophysical and ecological overview of the area, co-authored by the Nunatsiavut Government, Fisheries and Oceans Canada, and Environment and Climate Change Canada. Available information (including Local Knowledge [LK], peer-reviewed literature, archived scientific data from government and academia, and ongoing research), sensitive habitats and species, data gaps, and research recommendations are presented here for 14 biophysical, ecological, and social components of the study area: • Estuaries and coastal features; • Seabed features; • Sea ice; • Physical oceanography; • Biological oceanography; • Macrophytes; • Benthic communities; • Corals, sponges, and bryozoans; • Fish; • Marine mammals; • Marine birds; • Ecologically and Biologically Significant Areas; • Inuit use and other human activities; and • Protected areas and other closures.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".