{"id":"W4304809028","doi":"10.35848/1347-4065/ac99c2","title":"Control of growth interface shape during InGaSb growth by vertical gradient freezing under microgravity, and optimization using machine learning","year":2022,"lang":"en","type":"article","venue":"Japanese Journal of Applied Physics","topic":"Solidification and crystal growth phenomena","field":"Materials Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Crystal growth; Homogeneity (statistics); Materials science; Bayesian optimization; Growth rate; Interface (matter); Crucible (geodemography); Temperature gradient; Crystal (programming language); Rotation (mathematics); Computer science; Biological system; Mechanics; Composite material; Chemistry; Mathematics; Crystallography; Artificial intelligence; Geometry; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004288844,0.0001334496,0.0002827584,0.00006885915,0.0002986709,0.00005271743,0.0001975977,0.00002286235,0.00006937783],"category_scores_gemma":[0.0000231852,0.0001260306,0.00005548105,0.0002436079,0.00009840203,0.0001822644,0.0001386093,0.0002644364,6.837212e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001009695,"about_ca_system_score_gemma":0.00002335585,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002272012,"about_ca_topic_score_gemma":1.929136e-7,"domain_scores_codex":[0.9987122,0.0001007648,0.0004548469,0.0001675894,0.0003616411,0.000202949],"domain_scores_gemma":[0.9992334,0.00008631523,0.0003619898,0.00008356478,0.000132254,0.000102522],"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":[0.0002692104,0.0001301726,0.0007958451,0.00003077482,0.00002039717,9.844722e-7,0.001568213,0.1510942,0.8450413,0.001026788,0.000001319824,0.0000207784],"study_design_scores_gemma":[0.00326782,0.0002784094,0.0005680709,0.00002988182,0.0001048519,0.0001305695,0.004055044,0.1813593,0.8078625,0.002038227,0.000005345563,0.0002999537],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.976656,0.00007045329,0.02298154,0.00005159661,0.00005886975,0.000113703,0.000006779072,0.00001650282,0.00004460666],"genre_scores_gemma":[0.9985467,0.000006736071,0.001303391,0.00006422419,0.00004888526,0.000005131506,0.0000028379,0.00002039974,0.000001669076],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03717884,"threshold_uncertainty_score":0.5139379,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01182005121099419,"score_gpt":0.226162091939893,"score_spread":0.2143420407288988,"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."}}