{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001058988,0.0009320639,0.000733991,0.002341858,0.0004436336,0.001518144,0.0009754173,0.001430266,0.003951971],"category_scores_gemma":[0.002144936,0.0003915984,0.001201714,0.003844975,0.0007404587,0.002192152,0.0004614994,0.001361183,0.002071656],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006249609,"about_ca_system_score_gemma":0.0008549136,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003359716,"about_ca_topic_score_gemma":0.00173257,"domain_scores_codex":[0.9989342,0.0001310654,0.00007944018,0.0003343695,0.0004529533,0.00006798159],"domain_scores_gemma":[0.9983119,0.0004433079,0.0001053047,0.0001583577,0.0009301331,0.00005092287],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001299638,0.000122038,0.001686673,0.00107963,0.000122784,0.0001935122,0.0002003738,0.007976522,0.04333885,0.0094644,0.01075271,0.9249325],"study_design_scores_gemma":[0.00007610132,0.0008698486,0.01955708,0.0005783137,0.0004211527,0.002661343,0.0006564747,0.4624155,0.1646899,0.03445318,0.3133998,0.0002212379],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.02477142,0.0693391,0.8833173,0.002370199,0.001022537,0.0001992504,0.0003538991,0.001740257,0.01688607],"genre_scores_gemma":[0.2522426,0.09557105,0.6210781,0.001372277,0.001650907,0.0002963783,0.00166921,0.0004326346,0.02568682],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.003951971,"threshold_uncertainty_score":0.01322067,"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."}}