{"id":"W2109465025","doi":"10.1016/j.jenvman.2007.03.007","title":"Social Sciences and landscape analysis: Opportunities for the improvement of conservation policy design","year":2007,"lang":"en","type":"article","venue":"Journal of Environmental Management","topic":"Conservation, Biodiversity, and Resource Management","field":"Environmental Science","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Perspective (graphical); Politics; Process (computing); Public policy; Natural (archaeology); Sociology; Management science; Political science; Social science; Environmental planning; Environmental resource management; Economics; Computer science; Geography; Economic growth","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":[0.001771401,0.0001054667,0.000158502,0.0002307565,0.0003800011,0.00003343923,0.0002524782,0.00002561855,0.000136929],"category_scores_gemma":[0.000007542812,0.00007411019,0.0001257161,0.0002714679,0.0004782312,0.0001333074,0.0002503572,0.00004395987,0.000001378221],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001156372,"about_ca_system_score_gemma":0.000006175068,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004995934,"about_ca_topic_score_gemma":0.00001796183,"domain_scores_codex":[0.9988029,0.00002921898,0.0003847169,0.0001348603,0.000469279,0.0001790306],"domain_scores_gemma":[0.9993141,0.0001163648,0.0004321443,0.00008952121,0.000005996556,0.000041845],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006059991,0.0007466788,0.7480581,0.0001156735,0.003027047,0.00003237599,0.003074475,0.008587984,0.004321317,0.001334301,0.008580345,0.2215157],"study_design_scores_gemma":[0.0007242787,0.0003725048,0.9665661,0.000007222578,0.0008795636,0.000002371686,0.01261386,0.0009514267,0.000320315,0.0003778633,0.01706928,0.0001151736],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9498726,0.0002462386,0.040053,0.006356088,0.0000759969,0.0006783749,0.0000126546,0.000005818996,0.00269925],"genre_scores_gemma":[0.9960772,0.0004277891,0.001936916,0.0007878267,0.00005404493,0.000004279729,0.000001963829,0.000004219112,0.0007057039],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2214006,"threshold_uncertainty_score":0.3022126,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05311919543105613,"score_gpt":0.2556398043040788,"score_spread":0.2025206088730226,"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."}}