{"id":"W2942009392","doi":"10.1007/s11661-019-05227-8","title":"Evaluation of the Growth Kinetics of θ′ and θ-Al2Cu Precipitates in a Binary Al-3.5 Wt Pct Cu Alloy","year":2019,"lang":"en","type":"article","venue":"Metallurgical and Materials Transactions A","topic":"Aluminum Alloy Microstructure Properties","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Chicoutimi; Linamar (Canada); Rio Tinto (Canada); Université Laval","funders":"","keywords":"Materials science; Differential scanning calorimetry; Alloy; Precipitation; Kinetics; Microstructure; Indentation hardness; Transmission electron microscopy; Isothermal process; Quenching (fluorescence); Precipitation hardening; Kinetic energy; Thermodynamics; Metallurgy; Analytical Chemistry (journal); Chemistry; Fluorescence; Nanotechnology; Chromatography","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":[0.0001336486,0.000117471,0.0001191378,0.0001782062,0.0001985339,0.0002330089,0.0001519697,0.000168726,0.0007033874],"category_scores_gemma":[0.0003266746,0.0001139921,0.00007527763,0.0001430259,0.0001279729,0.0001537986,0.00006577903,0.0001317865,0.0001715459],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004169804,"about_ca_system_score_gemma":0.0002602209,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008528854,"about_ca_topic_score_gemma":0.008135645,"domain_scores_codex":[0.9999256,0.00000710759,0.00000453812,0.00001757361,0.00003061857,0.00001448472],"domain_scores_gemma":[0.9998246,0.00003947553,0.00002689649,0.00001142955,0.00008188534,0.0000157022],"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":[0.0002483826,0.00001576152,0.00100377,0.00003036335,0.000002191672,0.00003020381,0.00003980046,0.0004708707,0.9964575,0.00008584954,0.0000322054,0.001583209],"study_design_scores_gemma":[0.000006286279,0.0001543055,0.005470132,0.000001937698,0.000005410122,0.00002565079,0.00004782406,0.007460868,0.9863452,0.00001591062,0.0004631368,0.000003306501],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9981535,0.0001653691,0.0006689638,0.000009902547,0.000004709942,0.000007853321,0.00008276378,0.00002721964,0.0008796013],"genre_scores_gemma":[0.9987003,0.00005828845,0.0004650337,0.000003006962,0.000001090793,0.000003582125,0.00004944424,0.000007473397,0.0007118497],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008528854,"threshold_uncertainty_score":0.01695842,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01204274181836898,"score_gpt":0.2094058726314985,"score_spread":0.1973631308131295,"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."}}