From one to ten: Canada's approach to achieving marine conservation targets
Bibliographic record
Abstract
Abstract The Government of Canada has committed to the Convention on Biological Diversity (CBD) Strategic Plan for Biodiversity, which includes the Aichi Biodiversity Targets. Aichi Target 11 indicates that countries are to conserve at least 10% of coastal and marine areas, especially areas of particular importance for biodiversity and ecosystem services, by 2020. In 2015 Canada affirmed its commitment to the 10% target, and also committed to an interim target to protect 5% of coastal and marine areas by the end of 2017. The interim target was met in October 2017 through a combination of federal and provincial marine protected areas (MPAs) and fisheries area closures that qualify as other effective area‐based conservation measures (OECMs), which are referred to domestically as marine refuges. In 2016 the Government of Canada set out a five‐point plan for achieving its marine conservation targets, which includes finishing what was started, protecting large offshore areas, protecting areas under pressure, advancing OECMs and establishing MPAs faster. Key challenges that the Government of Canada faces in meeting its 2020 marine conservation target include balancing socio‐economic impacts with the need to conserve biodiversity and sustain ecosystem health and ensuring meaningful engagement with partners and stakeholders in a short period of time. Once Canada has met its 2020 marine conservation target it will continue to advance ongoing marine conservation initiatives, most notably the development of a national conservation network, and seek to ensure effective long‐term conservation through the management, monitoring and enforcement of established MPAs and OECMs.
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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 | 0.019 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.040 | 0.014 |
| Scholarly communication | 0.028 | 0.009 |
| Open science | 0.010 | 0.017 |
| Research integrity | 0.016 | 0.023 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".