{"id":"W6912424845","doi":"10.5281/zenodo.3790925","title":"Oxypoda gnara Casey 1911","year":2009,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Coleoptera Taxonomy and Distribution","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; Canadian Forest Service","funders":"","keywords":"Bionomics; Nothing; Margin (machine learning); Period (music)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0001152467,0.0005318315,0.0002478401,0.001602224,0.002221439,0.0004719142,0.0005954394,0.0003905488,0.02100988],"category_scores_gemma":[0.0003129525,0.0002058158,0.000121089,0.00150325,0.0007479257,0.0008320219,0.0006782493,0.0006903374,0.004198626],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002708146,"about_ca_system_score_gemma":0.0009418008,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2458082,"about_ca_topic_score_gemma":0.5274299,"domain_scores_codex":[0.9997848,0.000008272202,0.000009201092,0.00007784578,0.00007902704,0.00004093821],"domain_scores_gemma":[0.9998234,0.00001299434,0.00006024265,0.00001597733,0.00006830971,0.0000191958],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002584501,0.00009543714,0.1029713,0.000543843,0.00003319238,0.003092301,0.002560545,0.0003152474,0.0090927,0.008065405,0.2121479,0.6608236],"study_design_scores_gemma":[0.00002511992,0.00006260918,0.244292,0.0002457407,0.00002701584,0.002429478,0.001032377,0.00009353453,0.0004979142,0.0005541185,0.750723,0.00001713529],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.190423,0.01652535,0.001942104,0.001517155,0.0009703023,0.0007809114,0.01265029,0.0004142638,0.7747765],"genre_scores_gemma":[0.7846607,0.01102981,0.005989601,0.001318776,0.0004508691,0.0002937774,0.01250032,0.00007681186,0.1836794],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.2458082,"threshold_uncertainty_score":0.4887549,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03620349480298774,"score_gpt":0.2169653216267567,"score_spread":0.180761826823769,"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."}}