{"id":"W1878245057","doi":"10.1002/jwmg.956","title":"Predicting mule deer recruitment from climate oscillations for harvest management on the northern Great Plains","year":2015,"lang":"en","type":"article","venue":"Journal of Wildlife Management","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Pacific decadal oscillation; North Atlantic oscillation; Wildlife; Geography; El Niño Southern Oscillation; Environmental science; Population; Spring (device); Climate change; Climatology; Wildlife management; Physical geography; Ecology; Biology; Demography; Geology; Meteorology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007029306,0.0002941024,0.0002404502,0.0006397244,0.0002451305,0.0005910641,0.0002164258,0.000250016,0.0008201438],"category_scores_gemma":[0.001393192,0.0001667016,0.0002642235,0.0004068732,0.0001814172,0.0003065291,0.0003375793,0.000200461,0.0001503949],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004067952,"about_ca_system_score_gemma":0.0002788337,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02198519,"about_ca_topic_score_gemma":0.04439448,"domain_scores_codex":[0.9998673,0.00005023828,0.00001153868,0.00003293642,0.00001907756,0.00001890463],"domain_scores_gemma":[0.9992884,0.0002394086,0.0002060315,0.0000401071,0.00007818216,0.0001478899],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002373687,0.00001725088,0.9972633,0.000003498045,0.00002502517,0.00003246314,0.00003806745,0.001084146,0.0003519053,0.00001001981,0.0001023159,0.001048253],"study_design_scores_gemma":[0.00000175723,0.000009858374,0.9954177,0.000001957326,0.000004153554,0.00001170281,0.00006344261,0.004353471,0.00003465914,0.00001312609,0.00008653057,0.000001575555],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9993511,0.00002923742,0.0001501086,0.00003494701,0.000002610604,0.000004083879,0.0002564088,0.000009874392,0.000161667],"genre_scores_gemma":[0.9993344,0.0000231342,0.0001765371,0.000007616224,0.000004824735,0.000006157891,0.0003364635,0.000002116516,0.0001087203],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02198519,"threshold_uncertainty_score":0.04371446,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06173332428468273,"score_gpt":0.26286061787878,"score_spread":0.2011272935940973,"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."}}