{"id":"W4394819660","doi":"10.1007/s12221-024-00540-5","title":"Analysis of Hydrothermal Aging Water of Fire-Protective Fabrics Using GC × GC–TOFMS and FID","year":2024,"lang":"en","type":"article","venue":"Fibers and Polymers","topic":"Forensic Fingerprint Detection Methods","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Materials science; Gas chromatography; Textile; Hydrothermal circulation; Mass spectrometry; Gas chromatography–mass spectrometry; Leaching (pedology); Flame ionization detector; Evolved gas analysis; Composite material; Pulp and paper industry; Chemical engineering; Environmental science; Chromatography; Thermal analysis; Chemistry","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":[],"consensus_categories":[],"category_scores_codex":[0.000561499,0.00008028561,0.0002125335,0.0002381277,0.0001440825,0.000034867,0.00004979937,0.00007154346,0.0001877914],"category_scores_gemma":[0.00003017911,0.00006616547,0.00009512095,0.0005912147,0.0004122145,0.0001042843,0.00003470998,0.00008738618,5.373578e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002953355,"about_ca_system_score_gemma":0.00003240975,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009579809,"about_ca_topic_score_gemma":0.0001941408,"domain_scores_codex":[0.9991622,0.0001402556,0.0001651377,0.000185111,0.0001723333,0.000174968],"domain_scores_gemma":[0.9996717,0.0001210788,0.00004888335,0.00008080478,0.00002222216,0.00005530918],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000091575,0.00002993089,0.01207257,0.0003581469,0.002780153,0.00001939747,0.2535937,0.001458474,0.08657444,0.002660416,0.00006828285,0.6402929],"study_design_scores_gemma":[0.001040307,0.0004589332,0.01215737,0.0007046759,0.005171519,0.0000208922,0.04634484,0.2506178,0.6585256,0.003508063,0.01980525,0.001644824],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9950307,0.001124525,0.001529038,0.0001991155,0.0002145543,0.00009175025,0.00001376376,0.00002514404,0.001771432],"genre_scores_gemma":[0.9982646,0.00005306745,0.001221439,0.00002755151,0.000042363,0.000001814974,0.000001089049,0.000008792701,0.0003793493],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.638648,"threshold_uncertainty_score":0.9970155,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02133278013486418,"score_gpt":0.3172691353609577,"score_spread":0.2959363552260935,"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."}}