{"id":"W4408957498","doi":"10.1111/cobi.70016","title":"Use of community characteristics to predict hunting and game harvests in western Amazonian forests","year":2025,"lang":"en","type":"article","venue":"Conservation Biology","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; McGill University","funders":"Japan Society for the Promotion of Science; Social Sciences and Humanities Research Council of Canada","keywords":"Livelihood; Wildlife; Geography; Socioeconomic status; Amazon rainforest; Household income; Socioeconomics; Ecosystem services; Wildlife conservation; Ecosystem; Ecology; Economics; Agriculture; Demography; Population","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006993389,0.000218329,0.0001731207,0.0009445259,0.0004096436,0.0005567606,0.0002934579,0.0002775707,0.0008598398],"category_scores_gemma":[0.004178185,0.0002418424,0.0002777422,0.0006912519,0.000312443,0.0004756956,0.0006127207,0.0002809046,0.00008664312],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002942707,"about_ca_system_score_gemma":0.0002577647,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02904721,"about_ca_topic_score_gemma":0.06372629,"domain_scores_codex":[0.9997308,0.0001104457,0.0000202542,0.00006386948,0.00003273431,0.00004184443],"domain_scores_gemma":[0.9980866,0.0005921536,0.0008201486,0.00009874316,0.0001710221,0.0002314668],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001791986,0.0000273646,0.9980341,0.000005303913,0.00002236895,0.0000306685,0.0003272337,0.00006509373,0.0001089849,0.00001749515,0.00002378888,0.001319713],"study_design_scores_gemma":[0.000003105017,0.00003411305,0.998103,0.000007479563,0.00001132347,0.00005455582,0.0006261346,0.001035255,0.00001741384,0.00004103282,0.00006412138,0.000002424639],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9996885,0.00004358627,0.00006679923,0.0000179323,4.115224e-7,0.000006893824,0.00005147525,0.000001627646,0.0001228918],"genre_scores_gemma":[0.9997396,0.00002267848,0.0001201623,0.000004282525,7.379894e-7,0.00000676856,0.00007426882,6.099406e-7,0.00003096138],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02904721,"threshold_uncertainty_score":0.0577563,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03238652623741885,"score_gpt":0.2691167611799342,"score_spread":0.2367302349425154,"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."}}