{"id":"W4409318887","doi":"10.1038/s43247-025-02160-0","title":"High-resolution naturalness mapping can support conservation policy objectives and identify locations for strongly protected areas in France","year":2025,"lang":"en","type":"article","venue":"Communications Earth & Environment","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bell (Canada)","funders":"Université de Toulouse; University of Leeds; Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement; UK Research and Innovation","keywords":"Naturalness; Environmental resource management; Resolution (logic); Nature Conservation; Geography; Computer science; Environmental planning; Remote sensing; Environmental science; Physics; Artificial intelligence; Ecology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002519955,0.0001301947,0.0001519764,0.0001229097,0.0004293221,0.00004625453,0.0003745874,0.00007562245,0.00005554994],"category_scores_gemma":[0.00003310991,0.0001312239,0.00002955852,0.0003051226,0.0001158868,0.0002320358,0.0003374983,0.0001337318,0.00002865229],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000196,"about_ca_system_score_gemma":0.0000365566,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003921255,"about_ca_topic_score_gemma":0.005605546,"domain_scores_codex":[0.9989619,0.0001143793,0.0003107671,0.000278186,0.0001176415,0.000217103],"domain_scores_gemma":[0.9989389,0.0001021273,0.000118388,0.0007853805,0.000009163402,0.00004601385],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005614382,0.0008246084,0.9201303,0.0003001768,0.0001372633,0.000001206328,0.003649738,0.02860025,0.01256808,0.008937679,0.0004822587,0.02431233],"study_design_scores_gemma":[0.0005682894,0.00002567008,0.9652514,0.00007204744,0.00001513943,0.000001202448,0.000277709,0.01416569,0.0004182244,0.001202703,0.01785551,0.0001464408],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9840856,0.0003657224,0.004507182,0.009100584,0.00004830693,0.001383577,0.00005322992,0.00003863935,0.0004172236],"genre_scores_gemma":[0.9940176,0.000357495,0.004168368,0.000223166,0.00001290078,0.0006967392,0.0001774538,0.000008877037,0.0003374269],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04512111,"threshold_uncertainty_score":0.592779,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01383499372922193,"score_gpt":0.2496872954603326,"score_spread":0.2358523017311107,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}