{"id":"W2048668222","doi":"10.1504/ijmpt.2005.005744","title":"Use of an axial magnetic field to suppress convection in the solvent of Ge<SUB align=right>0.98Si<SUB align=right>0.02 grown by the travelling solvent method","year":2005,"lang":"en","type":"article","venue":"International Journal of Materials and Product Technology","topic":"Solidification and crystal growth phenomena","field":"Materials Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Space Agency","keywords":"Crucible (geodemography); Materials science; Magnetic field; Convection; Hartmann number; Germanium; Solvent; Mass transfer; Condensed matter physics; Flow (mathematics); Fluid dynamics; Crystal (programming language); Intensity (physics); Heat transfer; Mechanics; Field (mathematics); Silicon; Chemistry; Optics; Physics; Metallurgy; Magnetohydrodynamics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001945963,0.0001990447,0.000412195,0.0003468832,0.00008082733,0.0001442136,0.0009395947,0.0001368646,0.000317419],"category_scores_gemma":[0.0002101476,0.0001270204,0.00006962546,0.0001940357,0.0001873001,0.000313667,0.000117424,0.0001581496,0.000006580897],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004943365,"about_ca_system_score_gemma":0.00005228485,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004825867,"about_ca_topic_score_gemma":0.00003285435,"domain_scores_codex":[0.9973888,0.0003663727,0.001123264,0.0003160925,0.0005582685,0.0002471405],"domain_scores_gemma":[0.9982537,0.0002055116,0.0007318099,0.0003406881,0.0004128885,0.00005542004],"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":[0.0003679936,0.0002593805,0.00002858923,0.00002440981,0.00003531602,0.000008372648,0.0008650739,0.0001663315,0.9900922,0.003350017,0.0009074381,0.003894943],"study_design_scores_gemma":[0.0005356805,0.0006054186,0.0003420896,0.00007125846,0.00003871872,0.0003193422,0.0001942697,0.000071323,0.9875998,0.002885018,0.007200912,0.0001361159],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9823499,0.0003513273,0.002197388,0.0135221,0.001046447,0.0003938574,0.00007818299,0.00001991609,0.00004082525],"genre_scores_gemma":[0.9960951,0.0002242458,0.002796244,0.0003818237,0.0004105322,0.00003053941,0.000008960357,0.00001622928,0.00003637496],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01374509,"threshold_uncertainty_score":0.517974,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01833416214678934,"score_gpt":0.2760675811977474,"score_spread":0.257733419050958,"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."}}