{"id":"W4251980527","doi":"10.32920/ryerson.14646681.v1","title":"The Effects Of Blast Lag In Abrasive Jet Machined Micro-Channel Intersections","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Erosion and Abrasive Machining","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Abrasive; Lag; Machining; Materials science; Brittleness; Channel (broadcasting); Jet (fluid); Mechanics; Mechanical engineering; Composite material; Computer science; Engineering; Metallurgy; Physics; Electrical engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0002319314,0.0002311274,0.0002706951,0.00005551455,0.0001347731,0.00006624338,0.0004334361,0.0001412891,0.0007156599],"category_scores_gemma":[0.0002816894,0.0001570858,0.0001688349,0.0001801085,0.0002050803,0.00005126972,0.001923074,0.0006581435,0.00006163526],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000140457,"about_ca_system_score_gemma":0.00002021489,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003252579,"about_ca_topic_score_gemma":0.01507142,"domain_scores_codex":[0.9985964,0.00018985,0.0003264498,0.0004301314,0.0002144377,0.0002426774],"domain_scores_gemma":[0.9988751,0.0004729073,0.0001625328,0.000409483,0.00001103853,0.00006895064],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00009318331,0.0007435032,0.3708706,0.0005196392,0.0001745477,0.0001349533,0.01680698,0.00904173,0.5836784,0.0003465592,0.007012342,0.01057747],"study_design_scores_gemma":[0.001346687,0.000203239,0.7427227,0.001343297,0.00006587188,0.0000231629,0.006205127,0.00509562,0.2395374,0.002007731,0.0005672421,0.000881892],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9825153,0.0001350971,0.001310325,0.0003745956,0.001066188,0.0003904114,0.000007832346,0.00003235116,0.01416794],"genre_scores_gemma":[0.9978506,0.0001467221,0.0003715248,0.0002619997,0.00002458119,0.00005175597,0.00001624579,0.00001867147,0.001257915],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3718521,"threshold_uncertainty_score":0.8410209,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006211626537517972,"score_gpt":0.2350702002600806,"score_spread":0.2288585737225626,"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."}}