{"id":"W4237868949","doi":"10.1515/iupac.88.0186","title":"Feed","year":2017,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; National Research Council Canada","funders":"","keywords":"Computer science; Extraction (chemistry); Process engineering; Throughput; Sample (material); Sample preparation; Chromatography; Chemistry; Engineering; Telecommunications","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001544931,0.002132309,0.001442565,0.004360239,0.0008514383,0.002788717,0.00256334,0.001913536,0.1562935],"category_scores_gemma":[0.0111534,0.0006052699,0.001358519,0.007790647,0.0003213474,0.002047136,0.002082277,0.001701519,0.175078],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001604589,"about_ca_system_score_gemma":0.002882544,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02009208,"about_ca_topic_score_gemma":0.03606311,"domain_scores_codex":[0.9980781,0.0003227204,0.0002710497,0.0006618333,0.0004575796,0.0002087014],"domain_scores_gemma":[0.9955966,0.001217244,0.0005430868,0.0009184989,0.001515807,0.0002087625],"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":[0.0001795179,0.00003056364,0.002170768,0.002356899,0.00008433549,0.00002809201,0.00003203463,0.000352061,0.0002923363,0.001118216,0.9776263,0.01572898],"study_design_scores_gemma":[0.0001174255,0.0000181358,0.002917481,0.0006957925,0.00004504818,0.00003823156,0.00006015432,0.0002116281,0.0003499365,0.001548598,0.9939778,0.00001975608],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001134599,0.0001116412,0.0001991157,0.00005322611,0.00002279682,0.00002023469,0.9978803,0.0003215736,0.001277692],"genre_scores_gemma":[0.0003227928,0.0001492373,0.0006134083,0.00008578922,0.000007154855,0.00009609825,0.9975424,0.00008253511,0.001100663],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8437065,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02417520271405105,"score_gpt":0.4620245618213349,"score_spread":0.4378493591072839,"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."}}