{"id":"W4313307649","doi":"10.1109/iceccme55909.2022.9988417","title":"Research in Image Processing for Pipeline Crack Detection Applications","year":2022,"lang":"en","type":"article","venue":"2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Moncton","funders":"","keywords":"Pipeline (software); Pipeline transport; Computer science; Image segmentation; Segmentation; Generalization; Artificial intelligence; Image (mathematics); Computer vision; Image processing; Pattern recognition (psychology); Engineering; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0005921923,0.0001796753,0.0001778836,0.0006378578,0.0004359977,0.0001461471,0.0008403354,0.00006357821,0.00002027841],"category_scores_gemma":[0.0000161838,0.0002147641,0.0000528141,0.0006387539,0.00003535859,0.0001568499,0.0003290128,0.001081145,0.000003272547],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005972323,"about_ca_system_score_gemma":0.00008077815,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001682016,"about_ca_topic_score_gemma":0.0000130862,"domain_scores_codex":[0.9985861,0.00005592069,0.0003466575,0.0002916552,0.0003183718,0.0004013417],"domain_scores_gemma":[0.9989463,0.0001967823,0.00005395859,0.0004817937,0.0002533581,0.00006782614],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006577965,0.0002776177,0.00002451805,0.0001407286,0.00007919451,0.000002931981,0.0004138798,0.1919147,0.03924351,0.218614,0.0008303328,0.5483928],"study_design_scores_gemma":[0.0003717479,0.0001234486,0.00003558363,0.00003383818,0.000006180434,0.00001247464,0.0001236098,0.9439437,0.0005046563,0.002194164,0.05245379,0.0001967831],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005243444,0.001488446,0.9889905,0.0008814843,0.0006184246,0.0009346237,0.00006778681,0.0003247086,0.001450551],"genre_scores_gemma":[0.9754574,0.001313244,0.02119705,0.00003511229,0.0002149175,0.00156813,0.0001084463,0.00004592479,0.00005976663],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9702139,"threshold_uncertainty_score":0.8757825,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03343604019549405,"score_gpt":0.3112365582632455,"score_spread":0.2778005180677515,"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."}}