{"id":"W4233335589","doi":"10.1515/iupac.88.0345","title":"Bioaccessibility","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); Process engineering; Sample (material); Throughput; Scale (ratio); Sample preparation; Biochemical engineering; Chromatography; Chemistry; Engineering; Physics","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":[0.002203034,0.002051143,0.002154936,0.005387646,0.0007713825,0.002753803,0.00271315,0.001785874,0.06090666],"category_scores_gemma":[0.01467806,0.0005872916,0.002538723,0.007930784,0.0004435843,0.002233518,0.002586991,0.002106382,0.05428645],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001647532,"about_ca_system_score_gemma":0.002966013,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01281551,"about_ca_topic_score_gemma":0.02050199,"domain_scores_codex":[0.997313,0.000411614,0.0005078673,0.0008998378,0.0006445433,0.0002231883],"domain_scores_gemma":[0.9936557,0.002096499,0.001183567,0.001205291,0.001620699,0.0002382803],"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":[0.0007882509,0.00007699495,0.01066399,0.02031124,0.0007066806,0.00008040227,0.00009151717,0.001141263,0.001333806,0.002828671,0.9295465,0.03243062],"study_design_scores_gemma":[0.0002442549,0.00004507098,0.01132868,0.002631585,0.0002668376,0.000118469,0.00006375181,0.0003239953,0.0007395434,0.002363909,0.9818261,0.00004784818],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001904236,0.0004612505,0.0001480098,0.00005434001,0.00002094367,0.00001958155,0.9982467,0.0001485645,0.0007101384],"genre_scores_gemma":[0.0009167261,0.0005652611,0.000661643,0.00009913402,0.0000105514,0.0001409087,0.9968856,0.00006332952,0.0006568482],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.06090666,"threshold_uncertainty_score":0.2037531,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03826548752273336,"score_gpt":0.4768407654765232,"score_spread":0.4385752779537899,"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."}}