{"id":"W2046248721","doi":"10.2193/2007-535","title":"A New Method for Estimation of Resource Selection Probability Function","year":2009,"lang":"en","type":"article","venue":"Journal of Wildlife Management","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":121,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Estimator; Likelihood function; Selection (genetic algorithm); Maximization; Function (biology); Computer science; Applied mathematics; Statistics; Mathematics; Mathematical optimization; Maximum likelihood; Biology; Machine learning","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001117961,0.00007682321,0.0001465389,0.00007308791,0.00006845671,0.00001058525,0.000110905,0.00005068374,0.000133285],"category_scores_gemma":[0.00007314139,0.0000691449,0.00008862484,0.0002235242,0.00001670614,0.0002573527,0.00002136194,0.00007705468,0.00000633125],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001484184,"about_ca_system_score_gemma":0.00001503314,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001257428,"about_ca_topic_score_gemma":0.000008017926,"domain_scores_codex":[0.9990273,0.00007925609,0.0004251565,0.000122941,0.0002335903,0.0001118156],"domain_scores_gemma":[0.9993055,0.00005751271,0.0004639436,0.00009511939,0.00002595761,0.00005194554],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0009500035,0.0003010133,0.03261118,0.00004865539,0.0001000389,0.000001310316,0.0002038833,0.1838237,0.0003134452,0.003525828,0.2212301,0.5568909],"study_design_scores_gemma":[0.0012955,0.001529083,0.8634076,0.00004123884,0.0002408817,0.00001644226,0.00007119513,0.02426752,0.0002160113,0.05527213,0.0535168,0.0001256349],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1035583,0.000008210038,0.8882853,0.00586151,0.0001172141,0.0003840192,3.737285e-7,0.00001170009,0.001773337],"genre_scores_gemma":[0.3616733,0.000007641645,0.6329344,0.003904529,0.0001626153,0.000009629269,0.000003565408,0.00000818782,0.001296194],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8307964,"threshold_uncertainty_score":0.2819647,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01298762534612964,"score_gpt":0.261309753702509,"score_spread":0.2483221283563794,"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."}}