{"id":"W2099619608","doi":"10.1109/imtc.2005.1604471","title":"Quantifying Enhanced Visual Inspection by Using A Laser Displacement Sensor","year":2006,"lang":"en","type":"article","venue":"2005 IEEE Instrumentationand Measurement Technology Conference Proceedings","topic":"Optical measurement and interference techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Visual inspection; Displacement (psychology); Enhanced Data Rates for GSM Evolution; Aerospace; Computer vision; Calibration; Deformation (meteorology); Artificial intelligence; Laser; Computer science; Representation (politics); Visualization; Surface (topology); Materials science; Acoustics; Engineering; Optics; Aerospace engineering; Geometry; Physics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0003568447,0.0003503439,0.000238197,0.0006805806,0.00009879984,0.000478627,0.0004633933,0.0005431605,0.001057458],"category_scores_gemma":[0.001473077,0.0002062974,0.0001216169,0.0003410366,0.0003092194,0.000692511,0.0004738674,0.0003060099,0.0002501085],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000333159,"about_ca_system_score_gemma":0.0002590935,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005842185,"about_ca_topic_score_gemma":0.001070994,"domain_scores_codex":[0.9994436,0.00006842992,0.00001274366,0.00009852574,0.0003309824,0.0000457165],"domain_scores_gemma":[0.9991835,0.000309204,0.0001443153,0.00009415155,0.0002377544,0.00003107464],"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":[0.0001385937,0.00003633624,0.002469861,0.00008327918,0.00001010598,0.00006352747,0.0000881145,0.003349791,0.9345312,0.0004575915,0.0001616332,0.05860998],"study_design_scores_gemma":[0.00002155248,0.0005390592,0.01877019,0.00001988236,0.00002772962,0.0006393006,0.000105621,0.1187929,0.8585114,0.0005763088,0.00192269,0.00007332949],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6410207,0.000565123,0.355214,0.00009746537,0.00004304372,0.00005781641,0.0001117424,0.0007783013,0.002111784],"genre_scores_gemma":[0.889208,0.0002115633,0.1094925,0.00004715643,0.0000120823,0.00002521816,0.00007449873,0.00003157063,0.0008974508],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001057458,"threshold_uncertainty_score":0.003537536,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05068797147146627,"score_gpt":0.2918131754726475,"score_spread":0.2411252040011812,"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."}}