{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003735993,0.0002098861,0.0003329006,0.0003213097,0.0004192846,0.0007973633,0.0004404492,0.000442694,0.001084785],"category_scores_gemma":[0.002313644,0.0002597009,0.0002081094,0.0003506587,0.0004857085,0.0005863204,0.0004287507,0.0006395171,0.0001852833],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000562303,"about_ca_system_score_gemma":0.0003150874,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001056482,"about_ca_topic_score_gemma":0.001137227,"domain_scores_codex":[0.9995046,0.00003886108,0.00002972765,0.00008955159,0.0002041906,0.0001331214],"domain_scores_gemma":[0.9970611,0.001700093,0.0006378911,0.0001518328,0.0002485681,0.0002004904],"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":[0.00261939,0.0003552533,0.01364348,0.0002659301,0.00003036882,0.00116812,0.000494273,0.02585131,0.932652,0.0007489217,0.0002531866,0.02191782],"study_design_scores_gemma":[0.0000515665,0.001591456,0.01776561,0.00001817575,0.00002545633,0.0001794945,0.000245024,0.0249047,0.9541962,0.0001896589,0.0008076006,0.00002506946],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9967396,0.0004861096,0.002090561,0.00002057417,0.00001662849,0.00001214834,0.00004236809,0.00007062692,0.0005213777],"genre_scores_gemma":[0.9986883,0.0001228372,0.0008391591,0.000007470129,0.000002909536,0.000004564674,0.0000274606,0.00001073983,0.0002966531],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001084785,"threshold_uncertainty_score":0.004079878,"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."}}