{"id":"W4243738270","doi":"10.1515/iupac.87.0306","title":"Hypercapnia","year":2016,"lang":"pl","type":"dataset","venue":"IUPAC Standards Online","topic":"History and advancements in chemistry","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada Research Chairs; University of Toronto","funders":"","keywords":"Glossary; Chemical nomenclature; Relation (database); Computer science; Psychology; Chemistry; Linguistics; Philosophy; Data mining; Organic chemistry","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":[0.000716526,0.001566997,0.001136519,0.002216729,0.000636675,0.001898227,0.001778116,0.001344494,0.06584296],"category_scores_gemma":[0.00667065,0.0004001033,0.001407714,0.002662095,0.0002753082,0.001389034,0.001457258,0.001620226,0.06389155],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009263363,"about_ca_system_score_gemma":0.001397764,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01008352,"about_ca_topic_score_gemma":0.01750774,"domain_scores_codex":[0.9990132,0.0001243798,0.0002083123,0.0003411612,0.0002064926,0.000106487],"domain_scores_gemma":[0.9974926,0.0007035562,0.0004312733,0.0005459199,0.0006540164,0.0001727422],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005124316,0.00006490403,0.005790264,0.00277089,0.0001106668,0.00005736693,0.00003036272,0.0005098818,0.0003243602,0.0004964817,0.9694747,0.01985759],"study_design_scores_gemma":[0.0004884801,0.00009313413,0.03083559,0.001468647,0.0001103758,0.0003170617,0.0001190772,0.0008544477,0.001033727,0.002402223,0.9621783,0.00009906073],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0005268332,0.0002730684,0.00013847,0.00008310391,0.0000815267,0.00004393228,0.9962994,0.0004438183,0.00210972],"genre_scores_gemma":[0.002035168,0.0002699953,0.0004998947,0.0001620716,0.00003553615,0.0002218488,0.9951622,0.00006913741,0.00154416],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.06584296,"threshold_uncertainty_score":0.2202666,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01473634156887713,"score_gpt":0.3933729524344831,"score_spread":0.378636610865606,"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."}}