{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000145175,0.00004365314,0.00006553737,0.00001806293,0.0001573239,0.000002825575,0.00002860351,0.00007966633,0.0007129337],"category_scores_gemma":[0.00005084628,0.00004431367,0.00001337429,0.0001596608,0.0001922622,0.00008209995,0.00002880851,0.00005536493,0.00005730052],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007908265,"about_ca_system_score_gemma":0.00000946255,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001989955,"about_ca_topic_score_gemma":0.001672787,"domain_scores_codex":[0.9995232,0.00007002769,0.0001388999,0.0001351846,0.00003804807,0.00009461476],"domain_scores_gemma":[0.9997674,0.0000812725,0.00006370178,0.0000634277,0.000008049351,0.00001610702],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001019927,0.0000837261,0.9895267,0.000002360897,0.000002701238,1.269016e-7,0.00001206745,0.0017008,0.0002457795,0.001977241,0.005516864,0.000921391],"study_design_scores_gemma":[0.0001244592,0.0000670872,0.9772755,6.6528e-7,0.00001199703,0.000006589852,0.000005027235,0.007540552,0.0001811947,0.0127545,0.001994123,0.00003828483],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9903978,0.000003202629,0.005350908,0.001003015,0.00004477459,0.0001356614,0.000006376976,0.00002933341,0.003028975],"genre_scores_gemma":[0.9978539,3.804961e-7,0.001468039,0.00004759381,0.00001448408,0.00002925285,0.00005536309,0.000002031339,0.0005289404],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01225122,"threshold_uncertainty_score":0.7806123,"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."}}