{"id":"W7102564815","doi":"10.5683/sp3/pi7ywo","title":"Global drivers of echolocating mammal species richness","year":2025,"lang":"","type":"dataset","venue":"Borealis","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Natural Science Foundation of China","keywords":"Species richness; Mammal; Species diversity; Species distribution; Distribution (mathematics); Biodiversity; Global warming; Climate change","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":[0.0006503441,0.0006167784,0.0006147511,0.001638022,0.0004710019,0.001288339,0.0009989381,0.0005748094,0.02003856],"category_scores_gemma":[0.00333536,0.0002967737,0.0007228468,0.003359085,0.0002385417,0.0007618077,0.001106507,0.0007218028,0.01073495],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006821196,"about_ca_system_score_gemma":0.0006146629,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08406238,"about_ca_topic_score_gemma":0.09607951,"domain_scores_codex":[0.9996073,0.00006012678,0.00003356634,0.0001451717,0.00007661856,0.00007720903],"domain_scores_gemma":[0.9988577,0.000285793,0.0002453973,0.0001486189,0.0003059298,0.0001565222],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0002182881,0.00005297121,0.117138,0.0009130291,0.0002933237,0.00008687854,0.000181019,0.00144401,0.0005284852,0.001474136,0.8653688,0.01230116],"study_design_scores_gemma":[0.000226742,0.00005324054,0.6189928,0.0004458913,0.0002175478,0.000244324,0.0005273056,0.002241927,0.0004075973,0.001235288,0.3753352,0.00007205692],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.008816891,0.0003196101,0.0001027234,0.0002457355,0.00003407523,0.000005752132,0.9887361,0.0001193512,0.001619749],"genre_scores_gemma":[0.02864197,0.0001644468,0.0003870483,0.0001348915,0.00002446699,0.0000538466,0.9691975,0.00005900485,0.001336737],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08406238,"threshold_uncertainty_score":0.1671462,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01566883953066707,"score_gpt":0.280258165507823,"score_spread":0.2645893259771559,"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."}}