{"id":"W2104128658","doi":"10.24908/pceea.v0i0.3852","title":"IMAGING OF CORROSION PITS ON METALLIC PIPES USING A LASER SCANNER","year":2011,"lang":"en","type":"article","venue":"Proceedings of the Canadian Engineering Education Association (CEEA)","topic":"Non-Destructive Testing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Scanner; Laser scanning; Raster scan; Corrosion; Raster graphics; Materials science; Displacement (psychology); Nondestructive testing; Laser; Optics; Computer science; Computer vision; Metallurgy; Physics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024953,0.0002324937,0.0002422721,0.0008724781,0.0002628168,0.0003153888,0.0003940764,0.0004297176,0.002672174],"category_scores_gemma":[0.0004792753,0.000377751,0.0002322338,0.0003977787,0.0003111493,0.0004532583,0.0004082191,0.0003475262,0.0006492266],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002301619,"about_ca_system_score_gemma":0.0005146048,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001112845,"about_ca_topic_score_gemma":0.002599468,"domain_scores_codex":[0.9997956,0.00001934107,0.000008180611,0.00003456677,0.0001242916,0.00001798301],"domain_scores_gemma":[0.9995744,0.0001568242,0.00004598977,0.00006084108,0.0001362126,0.00002582921],"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":[0.00004562686,0.0000181991,0.001618979,0.0000624054,0.000005850383,0.0002026945,0.0001570204,0.001758962,0.9658545,0.0004072952,0.0003667823,0.02950174],"study_design_scores_gemma":[0.00004844338,0.0005819665,0.02391493,0.00004899871,0.00003489745,0.002899694,0.0003447035,0.08908457,0.8698009,0.0007402732,0.01242364,0.00007695704],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5366589,0.0004473204,0.451263,0.0002815725,0.00004186515,0.0001846647,0.0003552757,0.003135249,0.007632207],"genre_scores_gemma":[0.541046,0.0005134059,0.4521708,0.0001011534,0.00003202544,0.0001747127,0.0002241214,0.0001695611,0.005568236],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002672174,"threshold_uncertainty_score":0.008939266,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01390556003556109,"score_gpt":0.2068723497788096,"score_spread":0.1929667897432485,"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."}}