{"id":"W4245032426","doi":"10.1515/iupac.88.1270","title":"Quickening","year":2017,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada Research Chairs; University of Toronto","funders":"","keywords":"Glossary; Terminology; Quickening; Relation (database); Computer science; Biology; Linguistics; Genetics; Philosophy; Data mining","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001673935,0.0009240988,0.00130715,0.000517073,0.000613244,0.0004568819,0.002069649,0.0007760378,0.005846383],"category_scores_gemma":[0.003304711,0.0008842932,0.0003661011,0.0001898593,0.0004540895,0.0002800022,0.0007003321,0.001599712,0.0003228798],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001166858,"about_ca_system_score_gemma":0.002320534,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008450071,"about_ca_topic_score_gemma":0.006483189,"domain_scores_codex":[0.9943802,0.0001657784,0.000720266,0.001024808,0.002730157,0.0009787766],"domain_scores_gemma":[0.9935859,0.00009982558,0.001187313,0.003794732,0.0009386638,0.0003935684],"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":[0.0002602151,0.000213427,0.00001210799,0.0001752468,0.0002138255,0.0004289328,0.00001331525,0.000003815591,0.00001313041,0.000002892384,0.9976474,0.001015672],"study_design_scores_gemma":[0.001251104,0.000168453,0.00005017625,0.0007261898,0.0003561531,0.00005570084,0.00002010272,0.00001101292,0.00001187335,0.00007541196,0.9963303,0.0009435075],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00007594033,0.001206578,0.00001822281,0.0001596027,0.001666526,0.0004225082,0.9959579,0.0003060933,0.0001867035],"genre_scores_gemma":[0.00001194319,0.0002505108,0.0001504474,0.0001437715,0.002525649,0.00002155789,0.995901,0.0002683999,0.0007267104],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.005638182,"threshold_uncertainty_score":0.9993608,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02695527295356304,"score_gpt":0.469436438806088,"score_spread":0.442481165852525,"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."}}