{"id":"W4403383766","doi":"10.22541/au.172893961.15441112/v1","title":"AI-Enhanced Radioactive Particle Tracking: A Game Changing Methodology for Accelerating Industrial Process Development","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Graphite, nuclear technology, radiation studies","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Polytechnique Montréal","keywords":"Tracking (education); Process (computing); Particle (ecology); Computer science; Environmental science; Psychology; Geology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.001681297,0.0007751756,0.0006164558,0.0008386525,0.0006623107,0.0008404328,0.002304985,0.00105345,0.003701449],"category_scores_gemma":[0.003004289,0.0003612961,0.0007743064,0.0006840038,0.0009381406,0.001148674,0.00165439,0.001159476,0.0005554809],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008295805,"about_ca_system_score_gemma":0.001376512,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004549553,"about_ca_topic_score_gemma":0.005089293,"domain_scores_codex":[0.9992458,0.0002587324,0.00002949221,0.0001620366,0.0002340783,0.00006992556],"domain_scores_gemma":[0.9984629,0.000793985,0.0001683421,0.0001614784,0.0002832224,0.00013016],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002396699,0.0003816405,0.002343054,0.0001668441,0.00009687463,0.000211313,0.0002872733,0.6971408,0.01068952,0.05878833,0.003595009,0.2260598],"study_design_scores_gemma":[0.00001117505,0.00005913533,0.0001581849,0.000005928861,0.000008207485,0.0000202435,0.00001472625,0.9889838,0.001181239,0.007691605,0.001859079,0.000006629924],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0121234,0.00008503338,0.9833353,0.0001420627,0.00004522679,0.00009366776,0.00004173275,0.000384625,0.003748896],"genre_scores_gemma":[0.3378157,0.0001712689,0.6544033,0.0001908714,0.00005783763,0.0003887466,0.000172407,0.0001267398,0.006673104],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004549553,"threshold_uncertainty_score":0.01238257,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2531951232287129,"score_gpt":0.3923126760990052,"score_spread":0.1391175528702923,"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."}}