A Geospatial Approach for the Assessment and Management Prioritization of Philippine Terrestrial Key Biodiversity Areas: Towards Meeting Global Sustainability Targets
Bibliographic record
Abstract
Abstract. Biodiversity plays a major role in sustaining life on Earth, with innumerable benefits to society. However, biodiversity loss and extinction due to external threats have been increasing globally. Key biodiversity areas (KBAs), although without an established legal basis, are important sites that contribute to the persistence of biodiversity. The use of geospatial technology has been proven to be a reliable, cost-effective, and targeted approach for biodiversity conservation and ecological management. In this study, data integration and spatial analysis were used in developing an easily interpretable and adaptable quantitative assessment and prioritization of KBAs. The identification of priority KBAs was based on threatened species, human-made structures, forest fragmentation, and forest loss. The integrated rankings revealed that Sibutu and Tumindao, Ragay Gulf, and Simunul and Manuk Manka Islands were the three highest priority KBAs based on the integrated factor scores, with all having almost zero overlap with protected areas (PAs). Among the top twenty KBAs, twelve sites had less than 2% overlap with PAs. Priority KBAs were identified in this study, either by means of the integrated rankings or by analyzing the relationships of the factor values. Implementing a management system in these identified priority KBAs, either as PAs or other effective conservation measures (OECMs) will lead to improving the condition in these sites. Moreover, these additional areas for conservation can contribute towards SDG 15 and in meeting the Philippines’ commitment to the “30 by 30” target under the Kunming-Montreal Global Biodiversity Framework.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".