{"id":"W7130720418","doi":"10.1109/swc65939.2025.00038","title":"Automatic Defect Detection of Chain Link Fences Using Artificial Intelligence","year":2025,"lang":"","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Raytheon Technologies (Canada); University of Calgary","funders":"","keywords":"Process (computing); Segmentation; Task (project management); Anomaly detection; Categorization; Damages; Autoencoder; Enhanced Data Rates for GSM Evolution","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":[0.0003642463,0.0004965857,0.0005195242,0.001548401,0.0001891553,0.0005305029,0.0006793394,0.00071609,0.0004572329],"category_scores_gemma":[0.0006491099,0.0001981638,0.0004335141,0.0005714178,0.0003618582,0.0007066476,0.0003103121,0.0003148807,0.0001605072],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004435881,"about_ca_system_score_gemma":0.0004090935,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004132559,"about_ca_topic_score_gemma":0.005346549,"domain_scores_codex":[0.9997544,0.00002747827,0.00001201962,0.00007839187,0.00008914642,0.00003856859],"domain_scores_gemma":[0.9995949,0.0001084469,0.00007488604,0.000042124,0.0001590846,0.00002065676],"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":[0.0004807995,0.0002803938,0.01832971,0.0001446233,0.0001127774,0.0004559635,0.000162362,0.2916248,0.1559685,0.001543676,0.002193963,0.5287023],"study_design_scores_gemma":[0.000002909277,0.00004195943,0.004311556,0.000004532535,0.000009787446,0.00005326458,0.00002341589,0.9826229,0.01239702,0.0003332279,0.0001924148,0.000007012298],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5048466,0.0002682908,0.4916122,0.0001000816,0.00003757716,0.00004117083,0.00008280844,0.001654307,0.001356903],"genre_scores_gemma":[0.91781,0.00008738097,0.080768,0.0000297029,0.00001061335,0.00001519047,0.0001715941,0.0000343729,0.001073242],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004132559,"threshold_uncertainty_score":0.008217037,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03875716386074521,"score_gpt":0.3134590781955034,"score_spread":0.2747019143347582,"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."}}