{"id":"W4237682105","doi":"10.1515/iupac.88.0226","title":"Thermal Desorption","year":2017,"lang":"ru","type":"dataset","venue":"IUPAC Standards Online","topic":"Analytical chemistry methods development","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; National Research Council Canada","funders":"","keywords":"Computer science; Extraction (chemistry); Thermal desorption; Process engineering; Sample (material); Sample preparation; Microwave; Throughput; Desorption; Chromatography; Chemistry; Engineering; Adsorption","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":[0.002286959,0.002330761,0.001879015,0.003945489,0.001052,0.002519393,0.0024579,0.00142628,0.05199547],"category_scores_gemma":[0.006858238,0.0005591412,0.001714106,0.007592528,0.0003776132,0.001590307,0.00192888,0.001996905,0.06832568],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001250402,"about_ca_system_score_gemma":0.002394398,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009021614,"about_ca_topic_score_gemma":0.01667472,"domain_scores_codex":[0.9971893,0.0005117539,0.0003621356,0.00105492,0.0006394765,0.0002424151],"domain_scores_gemma":[0.9972839,0.0009273018,0.0004471161,0.0005660552,0.0006801614,0.00009539227],"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":[0.0008027223,0.0001103985,0.006660278,0.01271546,0.0004943698,0.00009201119,0.0001128991,0.001289051,0.003664877,0.002417441,0.9097881,0.06185246],"study_design_scores_gemma":[0.0001489785,0.000058972,0.007626513,0.0006443689,0.0001283836,0.0001023875,0.00006758852,0.0005122455,0.002833609,0.00214068,0.9856899,0.00004638137],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0005490614,0.0007051483,0.0008712715,0.00007451737,0.00004211999,0.00006355254,0.9946464,0.0008790979,0.00216883],"genre_scores_gemma":[0.001241789,0.0006625094,0.002066057,0.0001152939,0.00001378124,0.0002925643,0.9941724,0.0001619297,0.001273698],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.05199547,"threshold_uncertainty_score":0.1739422,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03237115837126048,"score_gpt":0.4403297158437546,"score_spread":0.4079585574724941,"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."}}