{"id":"W2147105060","doi":"10.1007/s00216-015-8683-5","title":"GC × GC–TOFMS and supervised multivariate approaches to study human cadaveric decomposition olfactive signatures","year":2015,"lang":"en","type":"article","venue":"Analytical and Bioanalytical Chemistry","topic":"Advanced Chemical Sensor Technologies","field":"Engineering","cited_by":72,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ontario Tech University","funders":"Office of Justice Programs; U.S. Department of Justice","keywords":"Robustness (evolution); Multivariate statistics; Gas chromatography; Decomposition; Normalization (sociology); Biological system; Computer science; Chemical process of decomposition; Artificial intelligence; Pattern recognition (psychology); Chemistry; Chromatography; Machine learning; Biology","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":[0.00007639919,0.0003234565,0.0004144887,0.00005380213,0.0000784203,0.0000707521,0.0001770406,0.00025437,0.00002619305],"category_scores_gemma":[0.000154444,0.0002682837,0.00005887549,0.0003165823,0.0002337593,0.00009470517,0.0001870305,0.0004308744,0.000007031178],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009449894,"about_ca_system_score_gemma":0.000004399972,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002909225,"about_ca_topic_score_gemma":0.000004077137,"domain_scores_codex":[0.9985297,0.00001550619,0.0002953669,0.0005028648,0.0002593235,0.000397217],"domain_scores_gemma":[0.9990708,0.0001032996,0.00002177225,0.0002540922,0.00004610519,0.000503947],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004552324,0.00186018,0.01314163,0.0005509463,0.001854186,0.0003652675,0.001125041,0.005297707,0.9556951,0.002840508,0.0009722747,0.01584192],"study_design_scores_gemma":[0.003665222,0.0005544108,0.01192662,0.0000752556,0.0007677786,0.00006591248,0.004468451,0.2782069,0.6885116,0.00930146,0.0004634201,0.001992974],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9966539,0.0001690851,0.0006288681,0.0004284032,0.00001365309,0.0001966579,0.00001608975,0.000351375,0.001541944],"genre_scores_gemma":[0.9987346,0.000008394523,0.001004508,0.0000431059,0.00006659788,0.00001589181,0.00001753703,0.00002718173,0.00008216502],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2729093,"threshold_uncertainty_score":0.9999769,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0733999442898681,"score_gpt":0.2753677425426501,"score_spread":0.201967798252782,"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."}}