{"id":"W2780826030","doi":"10.1007/s40962-017-0203-2","title":"Metallurgical Aspects of Inclusion Assessment in Al–6%Si Casting Alloy Using the LiMCA Technique","year":2017,"lang":"en","type":"article","venue":"International Journal of Metalcasting","topic":"Aluminum Alloy Microstructure Properties","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Materials science; Alloy; Metallurgy; Agglomerate; Casting; Refining (metallurgy); Machinability; Aluminium; Inclusion (mineral); Porosity; Non-metallic inclusions; Metal matrix composite; Foundry; Molten metal; Structural material; Composite material; Machining; Mineralogy","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":[0.0003580466,0.0001432188,0.0001833354,0.0004989849,0.0003245485,0.0002994099,0.0002125667,0.0002431828,0.001167597],"category_scores_gemma":[0.0004728483,0.0001732165,0.0001341706,0.0002402245,0.000245138,0.0002625836,0.0002014729,0.0002197968,0.0001757915],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002696151,"about_ca_system_score_gemma":0.0003874948,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00392372,"about_ca_topic_score_gemma":0.0134001,"domain_scores_codex":[0.9998469,0.00001576159,0.000009218837,0.00002573985,0.00008447116,0.00001783255],"domain_scores_gemma":[0.9996777,0.00007507329,0.00005058177,0.00002582289,0.0001564393,0.00001448939],"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":[0.0002431044,0.0000231416,0.003021205,0.00008285706,0.000005067813,0.0001014738,0.000197852,0.0005420372,0.9853955,0.0002477198,0.00006109293,0.01007888],"study_design_scores_gemma":[0.000007872115,0.0002343992,0.03242037,0.00001527909,0.00004180034,0.000278583,0.0002544993,0.01224926,0.9524886,0.0001012694,0.001891215,0.00001694607],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9918497,0.0004936023,0.005691447,0.00001772372,0.00001024045,0.00001666469,0.00005413884,0.00006446164,0.001801915],"genre_scores_gemma":[0.9938452,0.0001416641,0.004655291,0.000004531461,0.000003044334,0.000004121868,0.00003254324,0.00001260316,0.001300974],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00392372,"threshold_uncertainty_score":0.007801771,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03243046899692338,"score_gpt":0.3204135195250134,"score_spread":0.28798305052809,"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."}}