{"id":"W4406307888","doi":"10.1021/acs.oprd.4c00439","title":"Predicting Shock Sensitivity from Differential Scanning Calorimetry Data and Molecular Structure: Beyond the Yoshida Correlation","year":2025,"lang":"en","type":"article","venue":"Organic Process Research & Development","topic":"Thermal and Kinetic Analysis","field":"Materials Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Process Research Ortech (Canada)","funders":"","keywords":"Differential scanning calorimetry; Enthalpy; Sensitivity (control systems); Thermodynamics; Shock (circulatory); Logistic regression; Chemistry; Correlation; Calorimetry; Biological system; Materials science; Statistics; Mathematics; Physics; Internal medicine; Medicine","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":[],"consensus_categories":[],"category_scores_codex":[0.00117398,0.0001491351,0.0001907196,0.0001065191,0.0007323652,0.0002630595,0.0005748101,0.00008196445,0.0004483542],"category_scores_gemma":[0.0005851951,0.0001038038,0.00001292454,0.00062721,0.0001840545,0.0001830223,0.001236671,0.0003610999,0.00001967563],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009630572,"about_ca_system_score_gemma":0.000474706,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002475232,"about_ca_topic_score_gemma":0.0002064863,"domain_scores_codex":[0.9976469,0.0003093268,0.0002731823,0.0005849067,0.0007934602,0.0003922519],"domain_scores_gemma":[0.9988844,0.0002767166,0.00006972233,0.0004730391,0.0002083652,0.00008776481],"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":[0.00004679535,0.00002933659,0.02108023,0.00005408523,0.00009109543,0.00001284011,0.001708087,0.00003823476,0.9674606,0.00008762034,0.0001534223,0.009237653],"study_design_scores_gemma":[0.0003776609,0.00001454626,0.09108161,0.0001042951,0.00007661217,0.000005121602,0.001298989,0.01431616,0.8897202,0.002558709,0.000219665,0.0002264354],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9734356,0.0004033858,0.02509528,0.0004959016,0.0001319027,0.0002006394,0.00005607526,0.00003896432,0.0001422531],"genre_scores_gemma":[0.9988028,0.00001393327,0.000766606,0.00007420948,0.00006551302,0.000008232941,0.0001395094,0.00001278636,0.0001163805],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07774042,"threshold_uncertainty_score":0.5632835,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01893205407278611,"score_gpt":0.3046485404222326,"score_spread":0.2857164863494465,"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."}}