A Reference Guide on t he Use o f Indicators for Integrated Coastal Management
Bibliographic record
Abstract
This first issue is devoted to the use of indicators for ICAM, and is a direct result of the IOC-DFO-NOAA-CSMP International Workshop on the same topic, organised in May 2002, in Ottawa. Based on a background paper prepared by the Center for the Study of Marine Policy (University of Delaware) in preparation for the workshop, the aim of this Reference Guide is to present a literature review on the use of indicators around the world, from various programmes and projects, at global, regional, national and local scale.The need for indicators and reporting techniques which reflects the performance of coastal management projects and programmes and reveals the complex relationship that exist between coastal ecosystem health and anthropogenic activities, \nsocio-economic conditions and managerial decisions, has been reinforced recently by the World Summit on Sustainable Development’s Plan of Implementation. This Dossier will hopefully offer a first step towards the development of common practices and protocols in the application of such indicators.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.037 | 0.031 |
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".