{"id":"W4232086811","doi":"10.1515/iupac.88.0180","title":"Extract","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); Sample (material); Process engineering; Throughput; Chromatography; Chemistry; Engineering","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":[0.002182443,0.003286991,0.001886705,0.006238246,0.001235605,0.003467835,0.003471121,0.002729428,0.09659628],"category_scores_gemma":[0.01363542,0.0007409528,0.002034008,0.007871041,0.0005050775,0.002910692,0.003598652,0.002416556,0.1673746],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002108823,"about_ca_system_score_gemma":0.004448599,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01657297,"about_ca_topic_score_gemma":0.03597946,"domain_scores_codex":[0.9966878,0.0005050994,0.0005190794,0.001193152,0.0007387597,0.0003560158],"domain_scores_gemma":[0.9946383,0.001315472,0.0006385563,0.001331909,0.001797228,0.0002785283],"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":[0.0001607351,0.00002880945,0.002183487,0.002919779,0.0000888896,0.00003975412,0.00004018492,0.0003020288,0.0004106822,0.001357335,0.9806116,0.01185657],"study_design_scores_gemma":[0.0001302819,0.00001829154,0.002499893,0.0007981349,0.00005557188,0.00006840414,0.00007446516,0.0002339519,0.0004901893,0.002078748,0.9935259,0.0000261753],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001084395,0.0002073489,0.0002672527,0.0000950471,0.00004349806,0.00002901234,0.9976557,0.0004844088,0.001109233],"genre_scores_gemma":[0.0002446157,0.0001577822,0.0007348803,0.0001062544,0.00001095957,0.0001197034,0.9978388,0.00008899709,0.0006980261],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9034037,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02869086546927355,"score_gpt":0.4752512590657654,"score_spread":0.4465603935964919,"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."}}