{"id":"W4413134153","doi":"10.1186/s12302-025-01181-y","title":"Modeling and predicting land use and land cover changes using remote sensing in tropical coastal ecosystems of southern Peru","year":2025,"lang":"en","type":"article","venue":"Environmental Sciences Europe","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fisheries and Oceans Canada; University of British Columbia","funders":"University of Georgia","keywords":"Land cover; Land use; Geography; Ecosystem; Physical geography; Environmental science; Urban ecosystem; Period (music); Urbanization; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004951858,0.0005605983,0.0002468238,0.0009209623,0.0002593995,0.0009039987,0.0005183222,0.0004926066,0.0005385079],"category_scores_gemma":[0.001352866,0.0003284872,0.0006866513,0.0009228838,0.0002183836,0.0007538993,0.0005468672,0.0002746691,0.0001093727],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007518536,"about_ca_system_score_gemma":0.0005694393,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06301335,"about_ca_topic_score_gemma":0.06327689,"domain_scores_codex":[0.9998595,0.00006028701,0.000009863646,0.00003132016,0.00001409089,0.00002502002],"domain_scores_gemma":[0.9996693,0.0001774427,0.00006522737,0.00001682732,0.00004830305,0.00002296712],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0001244309,0.0002608355,0.4264706,0.0001563091,0.0002896337,0.000622138,0.0004177614,0.5468588,0.003296394,0.0005903379,0.0006409736,0.02027184],"study_design_scores_gemma":[0.00001915753,0.00006557487,0.1094506,0.00002096178,0.00005549341,0.00004912586,0.0004291717,0.8887084,0.0003876736,0.0002995074,0.0004967715,0.00001760852],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9978663,0.0001803772,0.001152903,0.0001290688,0.000002458612,0.00001203945,0.0001561664,0.00004913391,0.0004514415],"genre_scores_gemma":[0.9977604,0.0002033285,0.001569595,0.000009932302,0.00000433009,0.00002109097,0.0002412176,0.000006717751,0.0001833636],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06301335,"threshold_uncertainty_score":0.1252931,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01848431165067166,"score_gpt":0.2113355769228257,"score_spread":0.192851265272154,"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."}}