{"id":"W4230650191","doi":"10.1515/iupac.88.0161","title":"Partition","year":2017,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"South Asian Studies and Conflicts","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; National Research Council Canada","funders":"","keywords":"Partition (number theory); Computer science; Extraction (chemistry); Process engineering; Chromatography; Chemistry; Engineering; Mathematics","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":[0.001864325,0.002082289,0.001277277,0.003470356,0.000952149,0.003111099,0.002949965,0.001712818,0.1377081],"category_scores_gemma":[0.0132106,0.0005313063,0.002461994,0.00476026,0.000479581,0.002114647,0.002321142,0.001910554,0.1241234],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001716222,"about_ca_system_score_gemma":0.003805452,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01477329,"about_ca_topic_score_gemma":0.02769172,"domain_scores_codex":[0.9970674,0.0005785452,0.0004747682,0.001061282,0.0004799305,0.0003380891],"domain_scores_gemma":[0.9956698,0.001239725,0.0004803569,0.001054252,0.001310264,0.0002455485],"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":[0.0003411878,0.00004864613,0.004510171,0.002648775,0.0001218593,0.00003941907,0.00006684627,0.000354184,0.0001852919,0.002621435,0.9734578,0.01560433],"study_design_scores_gemma":[0.0003250571,0.00003852924,0.005231136,0.0009332052,0.00007191458,0.00008456896,0.0001621265,0.0003397372,0.0002907232,0.002825873,0.9896739,0.00002329294],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0003963509,0.0002514337,0.0003081277,0.0001839225,0.00007611958,0.00009781772,0.9953986,0.0003988381,0.002888792],"genre_scores_gemma":[0.001248896,0.0002263949,0.001038936,0.0002571511,0.00002963439,0.0004905079,0.9942714,0.0001228278,0.002314389],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1377081,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03645201543103407,"score_gpt":0.4888549221973992,"score_spread":0.4524029067663651,"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."}}