{"id":"W3015285002","doi":"10.3390/molecules25071719","title":"Green Approaches to Sample Preparation Based on Extraction Techniques","year":2020,"lang":"en","type":"review","venue":"Molecules","topic":"Analytical chemistry methods development","field":"Chemistry","cited_by":116,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Sample preparation; Sample (material); Environmentally friendly; Bioanalysis; Hazardous waste; Process engineering; Computer science; Biochemical engineering; Extraction (chemistry); Process (computing); Nanotechnology; Chemistry; Chromatography; Engineering; Materials science; Waste management","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001661516,0.00047908,0.0009120981,0.00008222698,0.00005796107,0.00005846419,0.0003464176,0.0004255974,0.000347524],"category_scores_gemma":[0.0005016198,0.0004335716,0.0003267929,0.0002515384,0.00002803617,0.00002865976,0.00009104953,0.0004341771,0.0001038987],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004037744,"about_ca_system_score_gemma":0.000229921,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001230227,"about_ca_topic_score_gemma":0.000001292346,"domain_scores_codex":[0.9980379,0.00007795142,0.0005218825,0.0007447092,0.000375851,0.0002416796],"domain_scores_gemma":[0.9986398,0.0003920721,0.0002298496,0.0005113053,0.00002808437,0.000198922],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002833996,0.00006016797,2.789822e-7,0.008244829,0.00006131513,0.00001641125,0.00001663975,0.000005709141,0.0001303763,0.00007835089,0.0002538345,0.9911038],"study_design_scores_gemma":[0.00003467028,0.00003561078,7.901469e-8,0.002226768,0.0002288344,0.000003961156,0.000006085482,0.000637658,0.01544015,0.00007449703,0.9808735,0.0004382475],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"methods","genre_scores_codex":[5.085635e-7,0.5308205,0.4111658,0.0004415781,0.0000387602,0.0007228237,0.0001984308,0.0006519223,0.0559596],"genre_scores_gemma":[0.00001598812,0.4401605,0.554806,0.0003211547,0.0005081795,0.001187547,0.001494321,0.0001964634,0.001309821],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.9906655,"threshold_uncertainty_score":0.9998116,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1982455443029129,"score_gpt":0.380781931222443,"score_spread":0.1825363869195301,"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."}}