{"id":"W2920862546","doi":"10.1016/j.msea.2019.03.015","title":"The effect of manganese on the microstructure and tensile response of an Al-Mg-Si alloy","year":2019,"lang":"en","type":"article","venue":"Materials Science and Engineering A","topic":"Aluminum Alloy Microstructure Properties","field":"Engineering","cited_by":85,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Materials science; Transgranular fracture; Metallurgy; Microstructure; Intergranular fracture; Ultimate tensile strength; Grain boundary; Manganese; Precipitation hardening; Electron backscatter diffraction; Alloy; Precipitation; Quenching (fluorescence); Slip (aerodynamics); Solid solution strengthening","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":[0.0001599563,0.0001365522,0.0001400153,0.0001178894,0.0001958524,0.0002145332,0.0002515688,0.000208942,0.001014149],"category_scores_gemma":[0.0004198736,0.0001553206,0.0001085624,0.0001108238,0.0002120133,0.0001487434,0.000112503,0.0001535645,0.0001149236],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003666084,"about_ca_system_score_gemma":0.0002002652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002707904,"about_ca_topic_score_gemma":0.005256669,"domain_scores_codex":[0.9999137,0.00001015338,0.000005665955,0.00001785246,0.00002960388,0.00002304545],"domain_scores_gemma":[0.9998026,0.00005593892,0.00004632977,0.00001591068,0.00005366798,0.00002549133],"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":[0.000560504,0.00002424285,0.001409478,0.00003251907,0.000009566927,0.00008921495,0.00002574704,0.0007648449,0.9959068,0.00005708701,0.00004568995,0.00107427],"study_design_scores_gemma":[0.00002306507,0.0006678308,0.0183847,0.000004463222,0.00002416754,0.00008679442,0.00007312803,0.007500336,0.9727183,0.00003219811,0.0004770589,0.000008024251],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9992378,0.00009738698,0.000101115,0.00002086782,0.000006619852,0.000002635971,0.00003254843,0.000009091756,0.0004919834],"genre_scores_gemma":[0.9994065,0.00003750359,0.0001046712,0.000008648706,0.000002039442,0.00000144091,0.00002106931,0.000003318335,0.0004149705],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002707904,"threshold_uncertainty_score":0.005384266,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002740848355253722,"score_gpt":0.1762964415856332,"score_spread":0.1735555932303794,"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."}}