{"id":"W1198650284","doi":"10.1016/j.chroma.2015.07.108","title":"Retention projection enables accurate calculation of liquid chromatographic retention times across labs and methods","year":2015,"lang":"en","type":"article","venue":"Journal of Chromatography A","topic":"Analytical Chemistry and Chromatography","field":"Chemistry","cited_by":52,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba","funders":"National Institute of Diabetes and Digestive and Kidney Diseases; National Institute of General Medical Sciences; National Institutes of Health; Beijing Jiaotong University Research Program","keywords":"Chemistry; Retention time; Kovats retention index; Chromatography; Projection (relational algebra); Analytical Chemistry (journal); Range (aeronautics); Knowledge retention; Algorithm; Computer science; Gas chromatography","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":[0.004739557,0.00171314,0.001002589,0.001264327,0.00111443,0.003964715,0.001686861,0.001505609,0.005193737],"category_scores_gemma":[0.01436044,0.001527593,0.0007726265,0.001537102,0.0008108179,0.003197762,0.002885788,0.003002961,0.005067862],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007002889,"about_ca_system_score_gemma":0.00387733,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001941672,"about_ca_topic_score_gemma":0.002491936,"domain_scores_codex":[0.9962204,0.0008252499,0.00021104,0.0008877736,0.001671576,0.0001840805],"domain_scores_gemma":[0.9945307,0.001683152,0.0005010426,0.001356308,0.001767887,0.0001608919],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006606678,0.0003643099,0.004893339,0.0004298471,0.000177453,0.0002408834,0.0003081412,0.009230803,0.453897,0.01288731,0.009529317,0.5073808],"study_design_scores_gemma":[0.00006271166,0.0002921052,0.005513045,0.00006980476,0.0001348752,0.0006804265,0.0001028071,0.2159969,0.7471582,0.01022508,0.01959373,0.0001704143],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01687811,0.0003004983,0.9727167,0.0001665825,0.00009447675,0.00008395615,0.0003591022,0.007189973,0.002210473],"genre_scores_gemma":[0.1125119,0.0005970956,0.8804559,0.0002412832,0.00006950559,0.0003108211,0.000772407,0.002255023,0.002786103],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005193737,"threshold_uncertainty_score":0.02506542,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03103906586786915,"score_gpt":0.326059360585442,"score_spread":0.2950202947175729,"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."}}