{"id":"W4388524490","doi":"10.1002/wlb3.01111","title":"Using individual‐based habitat selection analyses to understand the nuances of habitat use in an anthropogenic landscape: a case study using greater sage‐grouse trying to raise young in an oil and gas field","year":2023,"lang":"en","type":"article","venue":"Wildlife Biology","topic":"Rangeland and Wildlife Management","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Brood; Habitat; Population; Ecology; Nest (protein structural motif); Selection (genetic algorithm); Geography; Biology; Demography","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005506261,0.0001752198,0.0002606629,0.0003174717,0.0001884597,0.00007394549,0.0001529177,0.0000699732,0.00002483267],"category_scores_gemma":[0.00004958026,0.0001290684,0.00003129976,0.0009622566,0.0001101865,0.0003133404,0.0001669607,0.0001013344,0.000002324841],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000756273,"about_ca_system_score_gemma":0.00001349488,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.008310401,"about_ca_topic_score_gemma":0.1119457,"domain_scores_codex":[0.99835,0.0003904226,0.0003098672,0.0004630247,0.0001433341,0.0003433894],"domain_scores_gemma":[0.9994652,0.0001341341,0.00007415434,0.0002199008,0.000007696422,0.00009894035],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009068909,0.0001176814,0.9658241,0.0000094163,0.00001968743,0.0001630528,0.004936949,0.02388541,0.002955156,6.856338e-7,0.00003184017,0.001965381],"study_design_scores_gemma":[0.001360899,0.001130167,0.9416841,0.00006148843,0.00009367159,0.00007262491,0.02554735,0.02952665,0.000131285,0.00002816401,0.00003222925,0.0003314345],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9989227,0.00002688112,0.0003070465,0.0003284087,0.00007667478,0.0002931381,0.00001115383,0.00002564328,0.000008327774],"genre_scores_gemma":[0.9988073,0.00001429447,0.0004926328,0.0006134945,0.0000298344,0.00001486112,0.000005789284,0.00001446191,0.000007327373],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1036353,"threshold_uncertainty_score":0.9982933,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1509877964380018,"score_gpt":0.374230297160603,"score_spread":0.2232425007226011,"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."}}