{"id":"W4230177261","doi":"10.1515/iupac.88.0272","title":"Cartridge","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":"Cartridge; Computer science; Extraction (chemistry); Process engineering; Sample (material); Throughput; Sample preparation; Biochemical engineering; Chromatography; Engineering; Chemistry; Mechanical 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":[],"consensus_categories":[],"category_scores_codex":[0.003019218,0.00346714,0.002882924,0.005458011,0.001383473,0.003845677,0.004595604,0.002580634,0.1411989],"category_scores_gemma":[0.01267875,0.00108619,0.002456204,0.00966655,0.0005281664,0.002552034,0.003166637,0.002557046,0.2167568],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001762103,"about_ca_system_score_gemma":0.004611717,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01412396,"about_ca_topic_score_gemma":0.02810138,"domain_scores_codex":[0.9965227,0.0005308956,0.0005468568,0.001204533,0.0008687584,0.0003264307],"domain_scores_gemma":[0.9934463,0.001989276,0.0009399863,0.001421842,0.001893681,0.0003089806],"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":[0.0003936923,0.00005602268,0.002225333,0.004288624,0.0001728377,0.00004637981,0.00003554975,0.0003483758,0.0006958491,0.0008793183,0.9682568,0.0226012],"study_design_scores_gemma":[0.0001986477,0.00003351575,0.003864659,0.0006007182,0.0000945161,0.00008022082,0.00003729917,0.0002221272,0.000791804,0.00160467,0.992439,0.00003277447],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001597856,0.0003218494,0.0004581914,0.00007215935,0.00003154541,0.00005473952,0.9967521,0.0008282872,0.001321334],"genre_scores_gemma":[0.0003044831,0.0002597016,0.00142123,0.0001116828,0.00001207183,0.0001781384,0.9964913,0.0001682134,0.001053223],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1411989,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02611352102315978,"score_gpt":0.4687794535876121,"score_spread":0.4426659325644523,"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."}}