{"id":"W2141209297","doi":"10.1111/j.1467-8276.2007.00995.x","title":"Wildlife Damage and Agriculture: A Dynamic Analysis of Compensation Schemes","year":2007,"lang":"en","type":"article","venue":"American Journal of Agricultural Economics","topic":"Economic and Environmental Valuation","field":"Economics, Econometrics and Finance","cited_by":55,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Social Sciences and Humanities Research Council of Canada; Nederlandse Organisatie voor Wetenschappelijk Onderzoek","keywords":"Wildlife; Damages; Compensation (psychology); Wildlife conservation; Stock (firearms); Natural resource economics; Welfare; Business; Economics; Geography; Ecology; Market economy; Political science","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":[],"consensus_categories":[],"category_scores_codex":[0.0007147174,0.0001503489,0.0007407428,0.000336334,0.00005187975,0.00003273543,0.0001586481,0.00005124405,0.00005599223],"category_scores_gemma":[0.00002858326,0.000126389,0.000251413,0.0003713303,0.0001855537,0.0004127961,0.0000372866,0.0001257948,0.00001133164],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002179658,"about_ca_system_score_gemma":0.000008703909,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000108794,"about_ca_topic_score_gemma":0.0001192246,"domain_scores_codex":[0.9984194,0.00001437059,0.001159051,0.0001991733,0.00002979215,0.0001781852],"domain_scores_gemma":[0.9974381,0.00008516158,0.002202072,0.0001158995,0.00004145249,0.0001173709],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009499222,0.00022178,0.9424626,0.00001936403,0.003362342,0.000002487786,0.00172831,0.01302813,0.002559733,0.02761021,0.0002544146,0.008655663],"study_design_scores_gemma":[0.0003396849,0.0002002533,0.9953783,0.000006626817,0.0001770601,0.00001837196,0.001811336,0.0006995972,0.0002223079,0.0003538231,0.000614494,0.0001781603],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9970375,0.0003315458,0.001466085,0.0004544973,0.0001144452,0.00007243834,0.00005400172,0.000004680939,0.0004648258],"genre_scores_gemma":[0.9963273,0.0007169647,0.002589487,0.0002171214,0.0000517968,7.608637e-7,0.00004278911,0.000007726757,0.00004605652],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05291571,"threshold_uncertainty_score":0.5153992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01959887363649137,"score_gpt":0.2026950041531761,"score_spread":0.1830961305166847,"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."}}