{"id":"W4312281621","doi":"10.1115/msec2022-85334","title":"A Deterministic Inspection of Surface Preparation for Metalization","year":2022,"lang":"en","type":"article","venue":"Volume 1: Additive Manufacturing; Biomanufacturing; Life Cycle Engineering; Manufacturing Equipment and Automation; Nano/Micro/Meso Manufacturing","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Histogram; Thresholding; Coating; Computer science; Composite number; Materials science; Artificial intelligence; Computer vision; Composite material; Image (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":["metaepi_narrow","sts"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.001151929,0.001609458,0.001598669,0.001552312,0.001574959,0.0004760187,0.0007664325,0.000527949,0.0004326359],"category_scores_gemma":[0.0001256437,0.001817578,0.0006711673,0.0003527213,0.0001795105,0.001252295,0.0006027161,0.0009545701,0.00004191157],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001192437,"about_ca_system_score_gemma":0.0001360154,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001613823,"about_ca_topic_score_gemma":0.00001499526,"domain_scores_codex":[0.9927987,0.0002411187,0.002388738,0.001641307,0.001310015,0.001620133],"domain_scores_gemma":[0.9967062,0.0005065799,0.001061873,0.00101265,0.0001352319,0.0005774961],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006197747,0.0003195448,0.00009594365,0.002272243,0.001212474,0.00003762755,0.001841608,0.897513,0.07055087,0.0001187765,0.002575656,0.02284248],"study_design_scores_gemma":[0.002758745,0.0005970967,0.006366679,0.0002031731,0.0002802308,0.0001335107,0.0004095426,0.07070214,0.8873904,0.0002172997,0.02913403,0.001807144],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9592332,0.0004493913,0.02889798,0.00007433199,0.00409842,0.003131394,0.0009887806,0.00276384,0.0003627077],"genre_scores_gemma":[0.993926,0.0001267564,0.002710156,0.00006748489,0.0007259595,0.0008354536,0.000757849,0.0003965809,0.0004537484],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8268109,"threshold_uncertainty_score":0.9997249,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01001797617261706,"score_gpt":0.2174616093706726,"score_spread":0.2074436331980556,"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."}}