Environmental indicators: utility in meeting regulatory needs. An overview
Bibliographic record
Abstract
Abstract Rees, H. L., Hyland, J. L., Hylland, K., Mercer Clarke, C. S. L., Roff, J. C., and Ware, S. 2008. Environmental indicators: utility in meeting regulatory needs. An overview. – ICES Journal of Marine Science, 65: 1381–1386. The utility of environmental indicators in meeting regulatory needs was addressed at an international symposium held in November 2007. This paper summarizes the attributes and range of uses of indicators and highlights key points from theme sessions and a workshop on unifying concepts. The symposium attracted regulators and scientists, who supported the need to promote dialogue during the construction of indicator-based management frameworks and at key stages towards operational use. Scientists expressed willingness to engage with the wider societal context for indicator applications, which is essential to the development of ecosystem-based management. For the latter to be effective, more effort is needed to combine indicators with thresholds to guide management actions and, in the process, to assess the full range of consequences of non-compliance. There are clear benefits to periodic interdisciplinary reviews of progress in this area, and a follow-up event with a regulatory emphasis is suggested.
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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 | 0.040 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".