{"id":"W2187285270","doi":"","title":"10.3 RESOURCE SELECTION BY FEMALE GRIZZLY BEARS WITH CONSIDERATION TO HETEROGENEOUS LANDSCAPE PATTERN AND SCALE","year":2005,"lang":"en","type":"article","venue":"","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Vegetation (pathology); Terrain; Geography; Dominance (genetics); Resource (disambiguation); Scale (ratio); Selection (genetic algorithm); Ecology; Grizzly Bears; Physical geography; Environmental science; Ursus; Cartography; Population; Biology; Demography; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000296976,0.0001419865,0.0001808763,0.0003363017,0.0003816623,0.0004890412,0.000212305,0.0001443042,0.00165132],"category_scores_gemma":[0.0005110874,0.00009404334,0.0001856653,0.0002278179,0.0003545893,0.0001997392,0.0003176996,0.0001295855,0.0001703646],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002776347,"about_ca_system_score_gemma":0.0001572765,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01410668,"about_ca_topic_score_gemma":0.05551651,"domain_scores_codex":[0.9998935,0.00002417246,0.000003551034,0.00002783123,0.00002771366,0.00002313038],"domain_scores_gemma":[0.9996583,0.00006266555,0.0001388121,0.00003540905,0.00003702378,0.00006772959],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001006991,0.00002041629,0.9883723,0.000008646855,0.00002792291,0.0001036745,0.0003051251,0.0001916751,0.007162127,0.00005414732,0.00009549884,0.003557654],"study_design_scores_gemma":[8.029117e-7,0.0000159699,0.9995937,6.001935e-7,0.000002767786,0.00003094081,0.0001317718,0.00008505214,0.00007632879,0.0000125935,0.00004837758,0.000001012262],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9992594,0.00002116454,0.00006663933,0.00001288878,7.769298e-7,0.000002712142,0.00003479961,0.000001893682,0.0005994821],"genre_scores_gemma":[0.9993706,0.00001660054,0.0001836946,0.0000139543,0.00000290351,0.000004030256,0.0000747136,0.000001442257,0.0003321923],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01410668,"threshold_uncertainty_score":0.02804911,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00527906703674488,"score_gpt":0.188197583364501,"score_spread":0.1829185163277561,"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."}}