{"id":"W4412105478","doi":"10.1016/j.ces.2025.122173","title":"AI-enhanced radioactive particle tracking: A practical methodology for accelerating industrial process development","year":2025,"lang":"en","type":"article","venue":"Chemical Engineering Science","topic":"Graphite, nuclear technology, radiation studies","field":"Materials Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec; Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; OCP Group; Polytechnique Montréal","keywords":"Process (computing); Particle (ecology); Tracking (education); Process development; Process engineering; Computer science; Engineering; Programming language","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":[0.001053668,0.0001551912,0.0002553003,0.0001591252,0.0002557331,0.0001103729,0.0004031061,0.0001352416,0.00001299504],"category_scores_gemma":[0.007020855,0.0001486598,0.00003559471,0.0009887024,0.000431162,0.0003391138,0.0001272182,0.000254747,0.000007606429],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001737844,"about_ca_system_score_gemma":0.0003480451,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":6.905662e-7,"about_ca_topic_score_gemma":1.614357e-7,"domain_scores_codex":[0.9983634,0.00001849846,0.0003247353,0.0005209896,0.0002425177,0.0005298718],"domain_scores_gemma":[0.9988882,0.0005798683,0.00008419226,0.0001879253,0.0001734731,0.00008637523],"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":[0.00002755058,0.00003596696,0.0000682602,0.00002232687,0.000009072505,5.855021e-7,0.0005200832,0.0004469725,0.9919526,0.004474132,0.00006228657,0.002380158],"study_design_scores_gemma":[0.0004384013,0.00002072462,0.0002584392,0.00002893175,0.00001116283,0.000003922444,0.0002112173,0.007811257,0.9903054,0.0003854357,0.000366893,0.0001582347],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.910607,0.00002785746,0.08726003,0.0009993092,0.0004302715,0.0003050659,0.00000226358,0.0003002751,0.00006792841],"genre_scores_gemma":[0.89898,5.501327e-7,0.1006247,0.0001599083,0.0000483659,0.0001637209,7.034686e-7,0.000009846987,0.00001218521],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01336468,"threshold_uncertainty_score":0.8405127,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1133208702864557,"score_gpt":0.3702804081829186,"score_spread":0.2569595378964629,"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."}}