{"id":"W4411992669","doi":"10.1016/j.jlp.2025.105734","title":"Developing laser diffraction as an approach to categorize the dispersion propensity of combustible dust","year":2025,"lang":"en","type":"article","venue":"Journal of Loss Prevention in the Process Industries","topic":"Combustion and Detonation Processes","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University","funders":"Natural Science Research of Jiangsu Higher Education Institutions of China; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Dispersion (optics); Categorization; Diffraction; Environmental science; Optics; Physics; Computer science","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":[0.0009060854,0.0001122621,0.0001908025,0.0002478905,0.000138498,0.00008531898,0.000376611,0.00009473725,0.00001445425],"category_scores_gemma":[0.000258525,0.0000692867,0.00004222843,0.001096127,0.00005636288,0.0005580221,0.0000316649,0.0004376463,0.000001824638],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000887166,"about_ca_system_score_gemma":0.0002548087,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001010606,"about_ca_topic_score_gemma":0.00001078821,"domain_scores_codex":[0.9988534,0.000106168,0.0005018712,0.00009169054,0.0003293144,0.0001175805],"domain_scores_gemma":[0.9990972,0.00009241657,0.0002140546,0.0001351268,0.0004301349,0.00003112637],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.001654332,0.003212543,0.09057052,0.004472722,0.0006290843,0.00002474283,0.03168588,0.671838,0.001639439,0.06988636,0.01096299,0.1134234],"study_design_scores_gemma":[0.008736968,0.001290882,0.3465203,0.005715848,0.0008171528,0.0009004534,0.1361991,0.02031385,0.2785664,0.1695439,0.02947424,0.00192086],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.955712,0.000171694,0.03977253,0.00182688,0.0002885339,0.0003179586,0.00000152159,0.00002550526,0.001883429],"genre_scores_gemma":[0.9991935,0.0000742295,0.0002111164,0.0001097619,0.00004834868,0.00001858511,0.000003571148,0.000007770242,0.0003331474],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6515241,"threshold_uncertainty_score":0.282543,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03857150262221701,"score_gpt":0.3051350589695965,"score_spread":0.2665635563473795,"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."}}