{"id":"W2087975376","doi":"10.1109/icecs.2011.6122385","title":"Image processing technique for segmenting microstructural porosity of laser-welded thermoplastics","year":2011,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Welding; Materials science; Pixel; Porosity; Thermoplastic; Image segmentation; Process (computing); Image processing; Noise (video); Segmentation; Laser; Composite material; Microstructure; Computer science; Artificial intelligence; Image (mathematics); Optics","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":[0.000302588,0.0004676801,0.0004446808,0.002216214,0.0002618368,0.000570344,0.0007744746,0.0009076915,0.0008230108],"category_scores_gemma":[0.0008126833,0.000256788,0.0004229338,0.001088185,0.0003836676,0.0006857174,0.0002979528,0.000544618,0.0005836141],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003365092,"about_ca_system_score_gemma":0.0004601519,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001215029,"about_ca_topic_score_gemma":0.001295139,"domain_scores_codex":[0.9997402,0.00001702703,0.00001703242,0.00005555673,0.0001415862,0.00002857482],"domain_scores_gemma":[0.9996352,0.0001039192,0.00006878881,0.00003702896,0.0001381316,0.00001689339],"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":[0.0001571609,0.00008495662,0.001209636,0.0002029216,0.00002955823,0.0001654329,0.0001278821,0.01153599,0.5177046,0.0009003273,0.00103092,0.4668506],"study_design_scores_gemma":[0.00002381314,0.0002714342,0.01461088,0.00004248164,0.00009728559,0.001310396,0.0001343899,0.482035,0.4941229,0.001324633,0.005975383,0.00005149228],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06657493,0.0005199529,0.9301667,0.0001044712,0.00002973949,0.00009185139,0.0001414458,0.001442411,0.0009283843],"genre_scores_gemma":[0.2542742,0.0006627118,0.7431594,0.00006202894,0.00005156371,0.0001229415,0.0003231535,0.00009271949,0.001251338],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002216214,"threshold_uncertainty_score":0.002753258,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02281152118790705,"score_gpt":0.2290501622795408,"score_spread":0.2062386410916337,"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."}}