High-resolution naturalness mapping can support conservation policy objectives and identify locations for strongly protected areas in France
Bibliographic record
Abstract
Abstract Intact natural landscapes are essential to both biodiversity conservation efforts and human well-being but are increasingly threatened and lack sufficient protection. Bold National and International protected area targets aim to address this problem, yet the question remains – where will these areas be located? Using France as a case study, we present a high-resolution method to map naturalness potential. The resulting map, CARTNAT, performs well at identifying areas which have already been recognised as worthy of strong protection, under both National and International designations, however, only 1% of the top 10% of high naturalness areas in France are currently strongly protected. CARTNAT is already being used to highlight potential sites for new protected areas supporting the French National Strategy for Protected Areas to 2030. We argue that spatially informed participatory decision making of this type has the potential to deliver on national and international protected area policy objectives.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".