{"id":"W1556911429","doi":"","title":"Analysis of Chemical Warfare Agents by GC-MS: Second Chemical Cluster CRTI Training Exercise","year":2005,"lang":"en","type":"article","venue":"Defense Technical Information Center (DTIC)","topic":"Pesticide Exposure and Toxicity","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Chemical warfare; Chemical Warfare Agents; Chemical agents; Cluster (spacecraft); Sample (material); Chemical safety; Suspect; Triage; Terrorism; Chemistry; Engineering; Computer science; Medical emergency; Biochemical engineering; Chromatography; Medicine; Political science; Law","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001092753,0.001389506,0.00111045,0.001779049,0.00140049,0.0007570328,0.001156469,0.001182331,0.005757178],"category_scores_gemma":[0.001347974,0.0005064256,0.0007363468,0.0009113789,0.0004100908,0.0004351784,0.001259168,0.001396587,0.003122023],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00117814,"about_ca_system_score_gemma":0.002839219,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01078525,"about_ca_topic_score_gemma":0.03289449,"domain_scores_codex":[0.9982367,0.00009230188,0.00005060891,0.0003586899,0.001023491,0.0002382433],"domain_scores_gemma":[0.998955,0.000153331,0.00005482943,0.0001146676,0.0005946918,0.0001274836],"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":[0.000675144,0.001136227,0.003530387,0.0004726591,0.00006553325,0.0003142996,0.000519687,0.0019144,0.8802223,0.0003307019,0.008586469,0.1022322],"study_design_scores_gemma":[0.000164351,0.003363703,0.02584094,0.00006701057,0.0000903468,0.0006385287,0.0003360912,0.004367992,0.8697041,0.0003229564,0.09496027,0.00014373],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6841078,0.00250588,0.2059318,0.002038839,0.0009520735,0.02227491,0.01760913,0.008370297,0.05620926],"genre_scores_gemma":[0.3596106,0.00238362,0.4960702,0.003042517,0.0002989422,0.00787032,0.02205756,0.001536575,0.1071298],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01078525,"threshold_uncertainty_score":0.02144498,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02452379111897136,"score_gpt":0.2397300203487466,"score_spread":0.2152062292297752,"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."}}