{"id":"W4250918720","doi":"10.1515/iupac.88.0194","title":"Microextraction (Methodological Approach)","year":2017,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Advanced Materials Characterization Techniques","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); Sample (material); Scale (ratio); Process engineering; Sample preparation; Biochemical engineering; Chromatography; Chemistry; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005339629,0.0004453096,0.0006949166,0.0001580906,0.0001212098,0.0001483503,0.0005013313,0.000644153,0.0007028788],"category_scores_gemma":[0.0005132654,0.0004226977,0.0001105961,0.000055644,0.0001019073,0.0002245187,0.0001109939,0.0005788641,0.000002965166],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002956481,"about_ca_system_score_gemma":0.0000651545,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000105897,"about_ca_topic_score_gemma":0.000009477784,"domain_scores_codex":[0.998367,0.00009230916,0.0004453305,0.0003872998,0.0003933295,0.0003147083],"domain_scores_gemma":[0.998496,0.00005686162,0.0002588221,0.0009370754,0.0001558135,0.00009543639],"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":[0.00003400034,0.00005844953,3.513807e-7,0.0003489148,0.00003783795,0.00001647973,0.00000329387,0.00009020173,0.005946556,0.000002249995,0.9911725,0.002289176],"study_design_scores_gemma":[0.0001740063,0.0000417117,0.00003016208,0.00009943324,0.00004851383,0.00003250613,0.000003359633,0.0000662517,0.003091947,0.0001658621,0.9958175,0.0004287791],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00007791293,0.0001811457,0.0978803,0.00001889376,0.001120238,0.0002944592,0.8995263,0.0008316288,0.00006913333],"genre_scores_gemma":[0.00002058071,0.001888977,0.01441451,0.0000418219,0.0008335866,0.00004385204,0.9826013,0.00007563267,0.00007974872],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08346579,"threshold_uncertainty_score":0.9998225,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05255623133955686,"score_gpt":0.450732558199888,"score_spread":0.3981763268603311,"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."}}