{"id":"W2800298743","doi":"10.1002/jwmg.21471","title":"Predicting positive outcomes for waterfowl hunters and waterfront residents","year":2018,"lang":"en","type":"article","venue":"Journal of Wildlife Management","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"U.S. Fish and Wildlife Service","keywords":"Waterfowl; Wildlife; Geography; Harassment; Wildlife management; Fishery; Wildlife conservation; Hunting season; Socioeconomics; Environmental planning; Environmental resource management; Ecology; Political science; Habitat; Sociology; Environmental science; Demography; Population","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004703884,0.0001866154,0.0001016551,0.0007471631,0.0006530203,0.0005786645,0.000279972,0.0003303177,0.003411895],"category_scores_gemma":[0.002088115,0.000131928,0.0001663234,0.0004825647,0.0004531382,0.0004704487,0.0006350947,0.000346767,0.0001889028],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005504327,"about_ca_system_score_gemma":0.0004292349,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06373319,"about_ca_topic_score_gemma":0.1554745,"domain_scores_codex":[0.9997613,0.00005529552,0.00002153192,0.00004437479,0.00004615498,0.00007133545],"domain_scores_gemma":[0.9984534,0.0001873261,0.000652144,0.0000477017,0.0001792736,0.0004801668],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000007462382,0.00001720498,0.998935,0.000002010479,0.000004651683,0.00002748924,0.0001532569,0.00001435946,0.00004632861,0.000005439249,0.00008179283,0.0007049399],"study_design_scores_gemma":[0.000001214758,0.00001699091,0.9986084,0.000005372052,0.000002797454,0.0000270848,0.001108353,0.0001155626,0.00001711773,0.00001279117,0.00008317887,0.000001296142],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9994591,0.00001820164,0.0000346971,0.00004285651,0.000002003639,0.000006872788,0.00006193814,9.3339e-7,0.0003734424],"genre_scores_gemma":[0.9995553,0.00002343383,0.00009386674,0.00001265462,0.00000232673,0.000008742782,0.0001022188,4.519842e-7,0.000200945],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06373319,"threshold_uncertainty_score":0.1267244,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009374723063829086,"score_gpt":0.2368652072024848,"score_spread":0.2274904841386557,"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."}}