{"id":"W4244062685","doi":"10.1515/iupac.88.0301","title":"Fiber Format","year":2017,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Analytical chemistry methods development","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; National Research Council Canada","funders":"","keywords":"Computer science; Extraction (chemistry); Throughput; Fiber; Sample (material); Microwave; Process engineering; Sample preparation; Data mining; Chromatography; Materials science; Engineering; Chemistry; 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":[],"consensus_categories":[],"category_scores_codex":[0.001569887,0.002095757,0.00130253,0.005610238,0.0009677049,0.002380257,0.002467259,0.001416222,0.1046113],"category_scores_gemma":[0.007049545,0.000560391,0.001191569,0.008359714,0.0003740487,0.002256203,0.00190687,0.001422148,0.1539952],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001611614,"about_ca_system_score_gemma":0.003003219,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01811841,"about_ca_topic_score_gemma":0.02909611,"domain_scores_codex":[0.9979635,0.0002784505,0.0003223875,0.0007165208,0.0004899024,0.000229213],"domain_scores_gemma":[0.995914,0.0007759717,0.0007051655,0.0008636536,0.001528907,0.0002122131],"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":[0.0002996301,0.00005005045,0.002512704,0.002813426,0.00009918886,0.00004509597,0.00004243435,0.0005570524,0.0009317287,0.001384873,0.9720715,0.01919227],"study_design_scores_gemma":[0.0001226719,0.00002741922,0.005341682,0.0004395146,0.0000417644,0.00005168978,0.00006139045,0.0002631473,0.0008402503,0.001574145,0.9912101,0.00002607746],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00015714,0.00008331486,0.0001991743,0.00003506936,0.00001756634,0.00002690077,0.9979115,0.000363806,0.00120547],"genre_scores_gemma":[0.0003043318,0.0001192042,0.0005470987,0.00003869374,0.000007352658,0.00008401648,0.9978272,0.00007853081,0.0009935616],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1046113,"threshold_uncertainty_score":0.3499598,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02779160619417572,"score_gpt":0.4424795763448869,"score_spread":0.4146879701507112,"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."}}