{"id":"W2284437112","doi":"","title":"CHARACTERISATION OF STEEL BLADE MICROSTRUCTURE PRODUCED USING LASER ENGINEERING NET SHAPE PROCESS","year":2014,"lang":"en","type":"article","venue":"","topic":"Additive Manufacturing Materials and Processes","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Society of Intestinal Research","funders":"","keywords":"Microstructure; Materials science; Direct metal laser sintering; Optical microscope; Lens (geology); Laser; Martensite; Near net shape; Metallurgy; Scanning electron microscope; Selective laser sintering; Composite material; Sintering; Optics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000255482,0.0001356869,0.0001730986,0.0004587898,0.0001771154,0.000212152,0.000171629,0.0002766032,0.0008940809],"category_scores_gemma":[0.0002764208,0.0001725209,0.000205467,0.0003943267,0.0001908166,0.0001482151,0.00009020922,0.0001547648,0.0002318643],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001719182,"about_ca_system_score_gemma":0.0001307294,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007793973,"about_ca_topic_score_gemma":0.002254329,"domain_scores_codex":[0.9998275,0.00001107299,0.00001243593,0.00003079419,0.0000999222,0.00001835179],"domain_scores_gemma":[0.9996917,0.000050628,0.00006671586,0.00002839031,0.0001462637,0.00001637536],"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":[0.00004135689,0.000006826754,0.001282643,0.00004823163,0.000002479868,0.00009303977,0.0001005387,0.0003120408,0.9954686,0.00005041431,0.00002684024,0.002566945],"study_design_scores_gemma":[0.00001169012,0.0008330909,0.07324029,0.00001276744,0.00002311459,0.0004726405,0.0002239733,0.003201414,0.9198943,0.00006776893,0.002005355,0.00001354876],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9919198,0.0003516405,0.006008369,0.00001244729,0.000008503109,0.00002662146,0.000229648,0.0000647374,0.001378135],"genre_scores_gemma":[0.9908212,0.0002657931,0.007156367,0.00001137645,0.000002846968,0.00001913649,0.0002589533,0.00002281373,0.001441607],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008940809,"threshold_uncertainty_score":0.002990961,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007695081027416712,"score_gpt":0.1992111453050341,"score_spread":0.1915160642776174,"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."}}