Development of a management-based ranking of beaches
Bibliographic record
Abstract
Abstract Beach rankings are very frequent on the internet; however, the information provided on how these rankings are made is often unclear and their content is mostly subjective. In addition, the vast majority of these rankings do not take into account the fact that beaches are coastal eco-systems. The aim of the research was to develop an objective framework to rank the quality of beaches worldwide. The framework integrates indicators to assess the socio-ecological system quality and can be used as a basis for effective beach management. The methodology involved the collection, evaluation and grouping of indicators into domains and categories. Moreover, a measurement technique and a 5-point rating score for each indicator was used. Weights were calculated for different beach types using an analytical hierarchical process and the methodology was validated by a focus group of beach management experts. The quality value of each beach was calculated through equations and the results were presented in graphs inspired by the Circles of Sustainability and the Ocean Health Index. The theoretical application was tested on Portuguese beaches. The framework presents a holistic assessment of four domains: Recreation, Protection, Conservation and Sanitary. The resulting Beach Ranking Framework (BRF) is an objective, holistic framework designed to communicate with society, unlike the existing beach quality assessments.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".