{"id":"W1988545127","doi":"10.1016/j.optlaseng.2013.10.009","title":"Object shape-based optical sensing methodology and system for condition monitoring of contaminated engine lubricants","year":2013,"lang":"en","type":"article","venue":"Optics and Lasers in Engineering","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University; National Research Council Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Lubricant; Materials science; Computer science; Object (grammar); Computer vision; Optics; Artificial intelligence; Composite material; Physics","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":[0.0003159835,0.00013968,0.0003073589,0.0002025799,0.00002638358,0.0000289919,0.00002613038,0.0001503548,0.000001252458],"category_scores_gemma":[0.0000873517,0.0001446081,0.00003029453,0.0001356998,0.00001696739,0.00007216362,0.00001015766,0.0001297975,4.070257e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004867925,"about_ca_system_score_gemma":0.000006588762,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003812976,"about_ca_topic_score_gemma":0.000001533706,"domain_scores_codex":[0.9992736,0.00002017228,0.0002959436,0.0001352284,0.00007021807,0.000204811],"domain_scores_gemma":[0.9994175,0.0003488887,0.00003526282,0.00007750023,0.00005515428,0.00006564566],"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":[0.00004260689,0.00001599416,0.001281095,0.002274103,0.0001121306,0.00001683249,0.0002735599,0.5703458,0.3851395,0.0005414057,0.00001436821,0.03994266],"study_design_scores_gemma":[0.0008325267,0.00007569382,0.00213837,0.0003514192,0.00001827443,0.00002033378,0.0003335276,0.9042659,0.09179918,0.000004943579,0.00001417659,0.0001455977],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8785567,0.000108684,0.1203702,0.00000454165,0.0005086659,0.0003089239,0.00000753909,0.00009180627,0.0000429156],"genre_scores_gemma":[0.9773862,0.00001414454,0.02246761,0.000001188043,0.00007243506,0.00002352357,0.000004093019,0.00002833573,0.000002408892],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3339202,"threshold_uncertainty_score":0.5896947,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02442344679991158,"score_gpt":0.2461756753726733,"score_spread":0.2217522285727618,"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."}}