{"id":"W3009190379","doi":"10.1002/ece3.6096","title":"Predictor species: Improving assessments of rare species occurrence by modeling environmental co‐responses","year":2020,"lang":"en","type":"article","venue":"Ecology and Evolution","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Socio-Environmental Synthesis Center; National Science Foundation","keywords":"Rare species; Species distribution; Ecology; Umbrella species; Indicator species; Conservation biology; Common species; Biology; Habitat; Endangered species","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":[0.002378538,0.0007386728,0.000565283,0.001343901,0.000427201,0.0008972547,0.0008156932,0.0006658784,0.001351195],"category_scores_gemma":[0.004434472,0.0003373892,0.0004932215,0.0009740369,0.0003663109,0.001474371,0.0008503679,0.0007364349,0.0002449193],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004606952,"about_ca_system_score_gemma":0.0008440643,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008637536,"about_ca_topic_score_gemma":0.01875642,"domain_scores_codex":[0.9994811,0.0002482834,0.00001940989,0.0001759676,0.00004961341,0.00002568665],"domain_scores_gemma":[0.9980626,0.001064144,0.0004351125,0.000178594,0.0001533176,0.0001062734],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002330257,0.0001985774,0.2678249,0.0001790794,0.000427433,0.0001397879,0.0002823394,0.6162116,0.003814347,0.004583734,0.00105417,0.105051],"study_design_scores_gemma":[0.00001118988,0.00004083411,0.0196871,0.00001989519,0.00005070265,0.00005602384,0.00005953534,0.9742939,0.000641892,0.004481836,0.0006389416,0.00001817425],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5392556,0.000324978,0.4554826,0.0002661961,0.00003214948,0.00007142012,0.001020977,0.0008926762,0.002653418],"genre_scores_gemma":[0.9037778,0.0001165073,0.09488363,0.00005270542,0.00001536582,0.00006115335,0.0005245767,0.00003878108,0.000529378],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008637536,"threshold_uncertainty_score":0.01717454,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02947520662640061,"score_gpt":0.2490205836048311,"score_spread":0.2195453769784305,"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."}}