{"id":"W4233152893","doi":"10.1111/conl.12705","title":"Subsidizing extinction?","year":2020,"lang":"en","type":"article","venue":"Conservation Letters","topic":"Economic and Environmental Valuation","field":"Economics, Econometrics and Finance","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fisheries and Oceans Canada; Western Forest Products; University of British Columbia","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Subsidy; Incentive; Harm; Natural resource economics; Accountability; Biodiversity; Government (linguistics); Public economics; Business; Action (physics); Work (physics); Economics; Environmental planning; Environmental resource management; Political science; Ecology; Geography","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0001298743,0.00006659026,0.0001125496,0.00003389614,0.00006485547,0.00003091107,0.00007145191,0.00002863713,0.001166091],"category_scores_gemma":[0.00004101136,0.00009087874,0.00004350054,0.00007828095,0.00002819419,0.0002619959,0.00001844611,0.00005662581,0.002512936],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005825085,"about_ca_system_score_gemma":0.000003063032,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002930314,"about_ca_topic_score_gemma":0.000001712912,"domain_scores_codex":[0.999374,0.000007290339,0.0002885235,0.0002137206,0.00001585148,0.0001006385],"domain_scores_gemma":[0.9996891,0.00001808921,0.0001466271,0.00009182419,0.000002901794,0.00005143078],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000006197718,0.0000105938,0.9563825,0.000009747761,0.00001837374,9.558809e-7,0.0004572578,0.000444224,0.003829712,0.01906832,0.01953893,0.0002331878],"study_design_scores_gemma":[0.0006682612,0.00002773949,0.8466268,0.000004012643,0.000004991992,0.000001561433,0.0001094084,0.01306278,0.0006143908,0.001664252,0.1369384,0.0002774312],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.900263,0.0001073365,0.02815801,0.06762862,0.0001723155,0.00009039766,0.00001020168,0.00005072888,0.003519437],"genre_scores_gemma":[0.9455637,0.00001617626,0.001100712,0.05305601,0.0001239474,0.00001188121,0.00002863858,0.00001165039,0.00008726037],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1173995,"threshold_uncertainty_score":0.999747,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1624033356003717,"score_gpt":0.2008509138346565,"score_spread":0.03844757823428482,"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."}}