{"id":"W3002237363","doi":"10.1111/oik.06932","title":"Matching habitat choice: it's not for everyone","year":2020,"lang":"en","type":"article","venue":"Oikos","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Habitat; Population; Matching (statistics); Ecology; Predation; Biology; Preference; Geography; Fishery; Demography; Statistics; Mathematics","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":[0.00254713,0.0002919821,0.0005968539,0.0004048767,0.002586046,0.001929985,0.0006125681,0.001453963,0.01339799],"category_scores_gemma":[0.006472845,0.0001770093,0.0003830492,0.0005398032,0.003779779,0.003777643,0.002539298,0.00170975,0.001973749],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006373886,"about_ca_system_score_gemma":0.0007748901,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002961189,"about_ca_topic_score_gemma":0.006278489,"domain_scores_codex":[0.9981545,0.0007073847,0.00006118901,0.0003323759,0.0003630535,0.000381541],"domain_scores_gemma":[0.9960802,0.0006329556,0.0005534712,0.0009454889,0.0005762068,0.001211599],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007996144,0.0003392955,0.3930299,0.0005255091,0.0008397636,0.001573413,0.02036614,0.00088937,0.01257197,0.06240147,0.1184238,0.3882398],"study_design_scores_gemma":[0.0001130062,0.0008067494,0.3415377,0.0009666945,0.0004986248,0.006489966,0.05538594,0.00211058,0.004073938,0.1437132,0.4439285,0.0003751354],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7092468,0.003737692,0.01783249,0.1348215,0.002777627,0.00008104859,0.0005379277,0.0004155524,0.1305494],"genre_scores_gemma":[0.9636882,0.000628566,0.002541413,0.01902137,0.0002137251,0.00003398151,0.0001205508,0.00008745006,0.01366475],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01339799,"threshold_uncertainty_score":0.04482073,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02286698671660788,"score_gpt":0.2460038096178068,"score_spread":0.2231368229011989,"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."}}