{"id":"W4386987288","doi":"10.3850/978-981-18-8071-1_p409-cd","title":"Optical Surface Analysis with Support Vector Machines based on Two Different Measurement Techniques","year":2023,"lang":"en","type":"article","venue":"","topic":"Textile materials and evaluations","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Gloss (optics); Surface roughness; Feature extraction; Process (computing); Artificial intelligence; Support vector machine; Machine learning; Surface finish; Image processing; Set (abstract data type); Computer vision; Data mining; Image (mathematics); Mechanical engineering; Engineering; Materials science","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000966117,0.0001773716,0.0002943407,0.0001535432,0.0001209333,0.000152065,0.0001745664,0.00003465184,0.007444975],"category_scores_gemma":[0.00005072476,0.0001070305,0.00008128196,0.0005164758,0.00005028966,0.00005783753,0.00004655722,0.00004153154,0.000508688],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007746081,"about_ca_system_score_gemma":0.00006169686,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001288936,"about_ca_topic_score_gemma":0.0003770084,"domain_scores_codex":[0.9981303,0.0001051372,0.0002491841,0.0003530342,0.0008715299,0.000290817],"domain_scores_gemma":[0.9992488,0.00006671654,0.00005730599,0.0003887477,0.00013919,0.00009922271],"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":[0.00009764382,0.000169331,0.003037842,0.00001914,0.0000598551,0.000008965488,0.00004360997,0.009592536,0.9842485,0.0007398441,0.00159026,0.0003924548],"study_design_scores_gemma":[0.0004035883,0.0003869243,0.06631476,0.00002304955,0.0003567442,8.150439e-7,0.00002321105,0.03153697,0.9003116,0.00009490279,0.0002575726,0.0002898615],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9874876,0.000001342516,0.007282668,0.00116171,0.0001408062,0.0002828115,0.0000457945,0.0006824122,0.002914816],"genre_scores_gemma":[0.9957224,0.000001209168,0.003599712,0.0001577624,0.00004678731,0.00006391919,0.00004613074,0.00001749585,0.0003446278],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08393691,"threshold_uncertainty_score":0.9934624,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04507894549339121,"score_gpt":0.3063852821125822,"score_spread":0.261306336619191,"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."}}