Scientific criteria for conservation and sustainable usage of marine biodiversity in Canada's oceans
Bibliographic record
Abstract
The Canadian Healthy Oceans Network is a new national marine science initiative that is uniting researchers to provide scientific guidelines for policy in conservation and sustainable use of marine biodiversity resources in Canada's three oceans. Theme Marine Biodiversity is addressing how patterns of biological biodiversity are related to habitat diversity. Specifically, we are testing hypotheses that link functional (ecological roles of different species) and species biodiversity to habitat complexity. Theme Ecosystem Function is determining how ecosystem function (processes such as nutrient cycling) and health (whether ecosystems are able to maintain these processes) are linked to biodiversity and natural and anthropogenic disturbances. Specifically we aim to understand the role of biodiversity in marine ecosystem services (the “goods” provided to humans by living organisms) by linking biodiversity and ecosystem function measures, and provide predictive models and tools to minimize anthropogenic impacts. Theme Population Connectivity is addressing how dispersal of marine organisms, typically by early life stages such as eggs and larvae, influences patterns of diversity, resilience, and source/sink dynamics (recruitment “hotspots” versus poor areas for new individuals) of species and biological communities. Specific goals are to evaluate the role of larval dispersal in regional source-sink species dynamics using existing management areas (e.g. marine protected areas) as model systems and compare different metrics of larval dispersal to estimate metapopulation (interlinked populations) connectivity. We will synthesize the outcomes of each of these themes across the Network to identify approaches to bridge science and policy.
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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 | 0.046 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.023 | 0.017 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| 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".