Indicators for ecosystem-based management on the Scotian Shelf: bridging the gap between theory and practice
Bibliographic record
Abstract
Abstract The need for a more integrated approach to ocean management is increasingly being recognized. Discussion on appropriate indicators and reference points supporting such an approach has focused on the technical merits of one set of metrics over another in servicing some management goal. Relatively little effort has been put into answering the question how one would use suites of indicators to meet the multiple conservation objectives defined in operational plans. Such an exercise is being undertaken on the eastern Scotian Shelf off Canada's east coast, as a national integrated management pilot. A number of ocean industries – fishing, oil and gas exploration, transport, and the military – utilize the area, a typical situation elsewhere in the world. A suite of conceptual ecosystem-level objectives has been identified to address biodiversity, productivity, and habitat issues. Operational objectives, which identify an indicator and reference points associated with each conceptual objective, are then stated. Utilizing this framework, individual ocean industry plans and activities can be reviewed in a consistent manner, to determine how they might be constrained by the conservation objectives for the area. Issues of spatial scale and cumulative impacts are addressed, and comment is made on future developments.
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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 | 0.027 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".