{"id":"W2668423657","doi":"10.1002/ece3.3122","title":"Relative Selection Strength: Quantifying effect size in habitat‐ and step‐selection inference","year":2017,"lang":"en","type":"article","venue":"Ecology and Evolution","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":256,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"U.S. Geological Survey","keywords":"Covariate; Selection (genetic algorithm); Inference; Statistics; RSS; Context (archaeology); Statistical inference; Econometrics; Computer science; Mathematics; Ecology; Geography; Machine learning; Artificial intelligence; Biology","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":[0.0004795738,0.0000950801,0.0001234091,0.00003919794,0.0007714888,0.00002543568,0.00005565105,0.0002073475,0.0001038294],"category_scores_gemma":[0.000893696,0.00009621879,0.00001299764,0.00007413665,0.0002626537,0.0006852478,0.00007412175,0.000218217,0.0000473726],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001691107,"about_ca_system_score_gemma":0.00001384972,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006239319,"about_ca_topic_score_gemma":0.02854547,"domain_scores_codex":[0.999171,0.0001917389,0.0001331968,0.0002691587,0.00005001981,0.0001848842],"domain_scores_gemma":[0.9993294,0.0004258678,0.0001253924,0.00007713837,0.000007967406,0.00003421303],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006804905,0.00002405892,0.9964851,0.000005539256,0.000006444477,9.999236e-7,0.00008948224,0.0000667096,0.0008252387,0.0009810152,0.0001084206,0.001338937],"study_design_scores_gemma":[0.000540601,0.0003131201,0.9869705,0.00001020435,0.00001446921,0.00001078229,0.00001985573,0.009512458,0.00005902703,0.002404761,0.00004880429,0.00009539873],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9977322,0.00002105701,0.0004387301,0.0005206967,0.0001767663,0.000186979,5.885191e-7,0.00002367468,0.0008993433],"genre_scores_gemma":[0.9992997,0.00002635558,0.0003767934,0.00005177884,0.00002420535,0.00003365216,0.000001830277,0.000003908132,0.0001817539],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02792154,"threshold_uncertainty_score":0.989181,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01360757930621634,"score_gpt":0.2589832695208897,"score_spread":0.2453756902146734,"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."}}