IDENTIFICATION AND PRIORITIZATION OF HOT SPOTS IN MANAGING RISKS TO BLACK SEA COASTAL REGIONS
Bibliographic record
Abstract
In implementation of the Bucharest Convention (http://www.blacksea-commission.org/), the first regional List of Black Sea (BS) Hot Spots (HSs) has been prepared in 1996. It included municipal and industrial sources of pollution located in the BS coastal zone. Since then, the regional HSs List has not substantially changed, although a few non-harmonised revisions have been undertaken at national levels. The non-harmonization occurred due to the lack of an agreed regional methodology, which would clearly specify the term ‘hot spot’ and give criteria for HSs identification and ranking in support of decision-making in BS protection. Recognising this gap in the knowledge-based management of BS land-based sources of pollution (LBSs), in 2015we developed such a methodology and undertook revision of the regional BS HSs List. To automate the HSs Methodology application we developed a unique BS LBSs Database and a HSs software. This paper presents our approach to hot spots evaluation, which is applicable to any other sea. Results of identification and prioritization of BS HSs are discussed in view of their crucial role in managing risks to coastal regions and in investment planning aimed at reduction of BS pollution.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".