{"id":"W4246591938","doi":"10.32920/ryerson.14663409","title":"Sculpting Desired Topographies Using Abrasive Jet Micro-Machining","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Erosion and Abrasive Machining","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Machining; Process (computing); Abrasive; Texture (cosmology); Computer science; Jet (fluid); Surface (topology); Mechanical engineering; Feature (linguistics); Surface finish; Materials science; Artificial intelligence; Geometry; Mechanics; Engineering; Image (mathematics); Mathematics; Physics","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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004713589,0.0005191285,0.0005270971,0.0001464385,0.0005140789,0.0004116769,0.0005755303,0.0003060359,0.005542586],"category_scores_gemma":[0.0001689358,0.0004743848,0.0003839682,0.0003785373,0.0002626061,0.0002154687,0.003692694,0.0009440599,0.00009293978],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001805217,"about_ca_system_score_gemma":0.00004672537,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001824729,"about_ca_topic_score_gemma":0.0004634219,"domain_scores_codex":[0.9971178,0.0002346463,0.0005213492,0.001066234,0.0004937889,0.000566242],"domain_scores_gemma":[0.9987109,0.0001368941,0.0003276183,0.0005988974,0.00002401517,0.0002016344],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001334704,0.0001181843,0.6426491,0.0001117207,0.0001129255,0.0001491934,0.003733802,0.02310476,0.3207923,0.0001109274,0.0006344279,0.008469244],"study_design_scores_gemma":[0.003426561,0.000218459,0.5546935,0.005901724,0.00094185,0.0004782563,0.03160328,0.1140257,0.2707875,0.003934046,0.00352348,0.01046554],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9640318,0.0002805942,0.008170629,0.0001266986,0.0006453497,0.0002717605,0.000008701399,0.0001905988,0.02627389],"genre_scores_gemma":[0.8939274,0.0000733458,0.1045108,0.0008000128,0.00009826983,0.00001419251,0.00005544468,0.00005921443,0.0004613092],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09634019,"threshold_uncertainty_score":0.9997708,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04064250745934243,"score_gpt":0.2845181922812966,"score_spread":0.2438756848219542,"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."}}