{"id":"W1963783872","doi":"10.1890/0012-9658(2006)87[3021:wdaeor]2.0.co;2","title":"WEIGHTED DISTRIBUTIONS AND ESTIMATION OF RESOURCE SELECTION PROBABILITY FUNCTIONS","year":2006,"lang":"en","type":"article","venue":"Ecology","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":223,"is_retracted":false,"has_abstract":true,"ca_institutions":"AXYS Technologies (Canada); University of Alberta","funders":"","keywords":"Selection (genetic algorithm); Resource (disambiguation); Estimator; Function (biology); Estimation; Wildlife; Wildlife management; Computer science; Ecology; Statistics; Geography; Mathematics; Biology; Machine learning; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.007118965,0.0006525164,0.0007894475,0.002193472,0.0003195918,0.001318149,0.001579521,0.0009635914,0.001315274],"category_scores_gemma":[0.0505627,0.0006622066,0.000554943,0.00184424,0.00148293,0.003255979,0.001256086,0.001058823,0.0003111096],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008086271,"about_ca_system_score_gemma":0.0006037753,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004775762,"about_ca_topic_score_gemma":0.002907835,"domain_scores_codex":[0.997247,0.001637421,0.0001269624,0.0004380337,0.00040268,0.0001479207],"domain_scores_gemma":[0.9712295,0.02459526,0.001559622,0.001588659,0.0008803746,0.0001465365],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001333882,0.00006814529,0.02049455,0.0001697938,0.000209211,0.0001845119,0.0003354051,0.6847774,0.002894178,0.1294446,0.0008164939,0.1604723],"study_design_scores_gemma":[0.00001432651,0.00003448357,0.005210813,0.00003021896,0.00001460171,0.000104021,0.00005494505,0.8805218,0.0007513555,0.1123875,0.0008436406,0.00003238171],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03409056,0.0001160364,0.9650995,0.00005173189,0.000004752718,0.00003055184,0.00007577636,0.0001004832,0.0004305643],"genre_scores_gemma":[0.6610423,0.000620519,0.3352499,0.00007992069,0.00004562085,0.0003529484,0.0008576168,0.00009433003,0.001656829],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007118965,"threshold_uncertainty_score":0.03764915,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005509276281427386,"score_gpt":0.1939951830833483,"score_spread":0.1884859068019209,"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."}}