{"id":"W4240407031","doi":"10.1515/iupac.88.0206","title":"Static Extraction","year":2017,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Microfluidic and Capillary Electrophoresis Applications","field":"Engineering","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); Sample preparation; Throughput; Scale (ratio); 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":[0.002604482,0.002844635,0.002103792,0.005466165,0.001212926,0.00280022,0.003078796,0.002411925,0.06115897],"category_scores_gemma":[0.01505135,0.000668444,0.002291147,0.007416358,0.0005326447,0.002187698,0.002649417,0.002182771,0.09961029],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001783133,"about_ca_system_score_gemma":0.004518265,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01166851,"about_ca_topic_score_gemma":0.02516735,"domain_scores_codex":[0.9965053,0.0006396835,0.0005862966,0.001208182,0.0007118316,0.0003488136],"domain_scores_gemma":[0.9948566,0.001607888,0.0005824742,0.001170378,0.001555195,0.0002275152],"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":[0.0004103161,0.00005592522,0.002981618,0.006859713,0.0002015272,0.00007343273,0.00004963043,0.0005368604,0.0007039367,0.001496175,0.9628004,0.02383048],"study_design_scores_gemma":[0.0002077046,0.00003212419,0.003717636,0.001189224,0.0001084833,0.00008984611,0.00006897622,0.0002471337,0.0006906848,0.001907526,0.9917074,0.00003326311],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002450073,0.0005584268,0.0003967361,0.0001040442,0.00005643047,0.00006257612,0.9964463,0.0004878518,0.001642586],"genre_scores_gemma":[0.0004667627,0.0003487651,0.001007539,0.0001138192,0.00001398041,0.0002330897,0.9967514,0.00007638356,0.0009882143],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.06115897,"threshold_uncertainty_score":0.2045971,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01039252317283808,"score_gpt":0.3717660032288447,"score_spread":0.3613734800560067,"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."}}