{"id":"W1887030867","doi":"10.5281/zenodo.1080195","title":"Image Thresholding For Weld Defect Extraction In Industrial Radiographic Testing","year":2007,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Welding Techniques and Residual Stresses","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Thresholding; Radiography; Welding; Radiographic testing; Extraction (chemistry); Artificial intelligence; Computer science; Computer vision; Image (mathematics); Materials science; Medicine; Metallurgy; Radiology; Chromatography; Chemistry","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":[0.001475737,0.0003836723,0.000529697,0.001176367,0.0002053395,0.0007358274,0.0006223022,0.0005610593,0.001044458],"category_scores_gemma":[0.006223504,0.0002236056,0.0002203123,0.0009547713,0.0005062664,0.0005648386,0.0004188775,0.0003269241,0.0003464729],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002747338,"about_ca_system_score_gemma":0.0003094132,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005875978,"about_ca_topic_score_gemma":0.000650926,"domain_scores_codex":[0.9989054,0.0003145592,0.00006651593,0.0001247573,0.0005243466,0.00006437643],"domain_scores_gemma":[0.9972553,0.001809515,0.0002229139,0.0002323775,0.0004216787,0.00005830494],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006021906,0.00007981209,0.00325013,0.0005765727,0.00004111675,0.0001981464,0.0001967854,0.01971561,0.4556879,0.002177449,0.0005259943,0.5169482],"study_design_scores_gemma":[0.00005535431,0.001162138,0.03589075,0.0001477446,0.0001741162,0.002782852,0.0002760811,0.4006854,0.5487115,0.005383643,0.004609746,0.000120707],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1743341,0.002219198,0.8202472,0.00009437493,0.00003729966,0.0001095568,0.00006699362,0.0008678184,0.002023482],"genre_scores_gemma":[0.6668054,0.001106741,0.330784,0.00004130756,0.00002269894,0.00004956742,0.000116154,0.0002057916,0.000868249],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001475737,"threshold_uncertainty_score":0.007804513,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06014601789147014,"score_gpt":0.2674982306374947,"score_spread":0.2073522127460245,"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."}}