Comparative Analysis of Methodologies for the Evaluation of Geosites in the Context of the Santa Elena-Ancón Geopark Project
Bibliographic record
Abstract
The Santa Elena province in Ecuador has great geodiversity potential, due to its geological characteristics and its coastal and marine context, as verified by publications about geosites, which have allowed the approach and development of initiatives in a context of geodiversity and sustainability.The aim of this article is to analyze 10 geosites from the Santa Elena province comparatively using the Brilha methodology, Geological and Mining Institute of Spain (IGME) methodology, and geosites assessment methodology (GAM) for the establishment of methodological considerations in the evaluation of geosites.These methodologies consider (i) 10 of the most representative geosites of the province for a comparative analysis; (ii) the application of the methodologies Brilha, IGME, and GAM to 10 geosites to establish the corresponding assessments; (iii) a comparative matrix of the results and analysis of the resulting assessment; and (iv) a proposal for the guidelines of an integrating methodology concerning geosites.The results show a similar ranking of 10 geosites, but highlight valuations that prioritize one aspect over another or focus on ecotourism aspects or geoconservation aspects.Based on the results and the comparative matrix, a method is structured integrating geodiversity, protection, geo-conservation, and geotourism aspects, which offer a different ranking of the considered geosites, being the most valued geosites the Chocolatera, Olón Cliff, Ancon Oilfield, and Manglaralto Coastal Aquifer.
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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 | 0.062 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".