Whole-ocean network design and implementation pathway for Arctic marine conservation
Bibliographic record
Abstract
Abstract Forestalling the decline of global biodiversity requires urgent and transformative action at all levels of government and society, particularly in the Arctic Ocean and adjacent seas where rapid changes are already underway. Amid growing scientific support and mounting pressure, the majority of nations have committed to the most ambitious conservation targets yet. However, without an approach that inclusively and equitably reconciles conservation and sustainable ocean use, these targets will likely go unmet. Here, we present ArcNet: a network design framework to help achieve ocean-scale, area-based marine conservation in the Arctic. The framework is centred around a suite of web-based tools and a ~ 5.9 million km 2 network of 83 priority areas for conservation designed through expert-driven systematic conservation planning using conservation targets for over 800 features representing Arctic biodiversity. The ArcNet framework is intended to help adapt to new and emerging information, foster collaboration, and identify tailored conservation measures within a global context at different levels of planning and implementation.
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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 | 0.014 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 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".