The Use of Criteria for Assessing Ecological and Biological Significance in Setting Conservation Objectives
Bibliographic record
Abstract
No abstracts are to be cited without prior reference to the author.A key step in the implementation of Canada’s Ocean Act was the establishment of five Large Ocean Management Areas (LOMA) in Canada’s three oceans (Fig 1). Within each LOMA the intent was to develop Integrated Management Plans. Each Integrated Management Plan would address all human activities conducted in within the LOMA. The Integrated Management Plan would be an integrative framework that would ensure that even though the management of different individual activities would continue to be done by different sectoral agencies at various levels of government, the objectives of the different agencies would be mutually compatible, broadly supported by all sectors and stakeholders, and in aggregate their ecological consequences would be sustainable.
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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 | 0.120 | 0.296 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.038 | 0.027 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 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".