Indicators of marine ecosystem integrity for Canada’s Pacific: An expert-based hierarchical approach
Bibliographic record
Abstract
There is great interest and rapid progress around the world in developing sets of indicators of marine ecosystem integrity for assessment and management. However, the complexity of coastal marine ecosystems can challenge such efforts. To address this challenge, an expert-based, hierarchical, and adaptive approach was developed with the objectives of healthy marine ecosystems and community partnerships in monitoring and management. Small sets of the top-ranked indicators of ecosystem integrity and associated human pressures were derived from expert-rankings of lists of identified candidate indicators of the status of, and pressures on, each of 17 ecosystem features, organized within 8 elements in turn within 3 overlapping aspects of ecosystem health. Over 200 experts played a role in rating the relative value of 1,035 candidate indicators. A panel of topic experts was assigned to each of the 17 ecosystem features to apply 21 weighted indicator selection criteria. Selection criteria and candidate indicators were identified through literature reviews, expert panels, and surveys, and they were evaluated in terms of the experts’ judgements of importance to the health of Canada’s Pacific marine ecosystems. This produced a flexible, robust, and adaptable approach to identifying representative sets of indicators for any scale and for any management unit within Canada’s Pacific. At the broadest scale, it produced a top 20 list of ecosystem state and pressure indicators. These top indicators, or other sets selected for smaller regions, can then guide the development of both regional and nested local monitoring programs in a way that maximizes continuity while including locally unique values. This hierarchical expert-based approach was designed to address challenges of complexity and scale and to enable efficient selection of useful and representative sets of indicators of ecosystem integrity while also enabling the participation of broad government and stakeholder communities.
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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 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.020 | 0.010 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".