{"id":"W4232239249","doi":"10.1515/iupac.88.0360","title":"On-Line Extraction","year":2017,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Image and Object Detection Techniques","field":"Computer Science","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); Scale (ratio); Sample preparation; Throughput; 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":[0.002853014,0.004749688,0.003109824,0.005201516,0.001527809,0.003487944,0.004927319,0.00332546,0.02579551],"category_scores_gemma":[0.009612757,0.0006965386,0.002708536,0.00638045,0.000845419,0.002534894,0.003454254,0.002818853,0.09018937],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002043024,"about_ca_system_score_gemma":0.003824178,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01329775,"about_ca_topic_score_gemma":0.02676133,"domain_scores_codex":[0.9956266,0.0005900413,0.0006434942,0.001607378,0.001022065,0.0005104994],"domain_scores_gemma":[0.9962137,0.0007583735,0.0003565336,0.001226515,0.001279174,0.0001657008],"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":[0.0003532688,0.0001102745,0.002566884,0.003816784,0.0001416687,0.0001160496,0.00004569574,0.0008242387,0.001365919,0.000899769,0.9597336,0.03002589],"study_design_scores_gemma":[0.0002553403,0.00006796156,0.005247775,0.0009148576,0.0001112955,0.0002874142,0.0001493658,0.001471077,0.003010206,0.002998397,0.9854139,0.00007234628],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0008007511,0.001117164,0.001609193,0.0002076403,0.0001907789,0.0001817837,0.9911708,0.002376332,0.002345528],"genre_scores_gemma":[0.0007445102,0.0003641009,0.002390547,0.0001254546,0.0000210363,0.0003247487,0.9948991,0.00010718,0.00102337],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02579551,"threshold_uncertainty_score":0.08629459,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02192138603307032,"score_gpt":0.4413078897360692,"score_spread":0.4193865037029989,"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."}}