{"id":"W2586355427","doi":"10.1007/978-3-319-52132-9_13","title":"Electromagnetic Levitation Refining of Silicon–Iron Alloys for Generation of Solar Grade Silicon","year":2017,"lang":"en","type":"book-chapter","venue":"˜The œminerals, metals & materials series","topic":"Silicon and Solar Cell Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Ferrosilicon; Silicon; Refining (metallurgy); Materials science; Metallurgy; Alloy; Hydrogen; Levitation; Crucible (geodemography); Photovoltaic system; Impurity; Electrical engineering; Chemistry; Engineering","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":[0.000107838,0.0002830178,0.0002371043,0.0003524149,0.0003221983,0.0003977337,0.0003323,0.0002872052,0.004944332],"category_scores_gemma":[0.0001239544,0.0001676276,0.0003527129,0.0002589503,0.0002047602,0.0004987355,0.0003283338,0.0005239497,0.001380909],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005594864,"about_ca_system_score_gemma":0.0002531547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006850841,"about_ca_topic_score_gemma":0.002783419,"domain_scores_codex":[0.9998852,0.000006717588,0.000003557732,0.00001455864,0.00007519021,0.0000147685],"domain_scores_gemma":[0.9999751,0.000006159835,0.000003348565,0.000003285631,0.00001048178,0.000001642426],"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":[0.000167626,0.00008969176,0.0002287722,0.0009357512,0.00003503404,0.0003322705,0.0003318246,0.004146247,0.7405831,0.02228546,0.01052387,0.2203404],"study_design_scores_gemma":[0.00001075934,0.0002391989,0.0009632525,0.00007070005,0.00002086353,0.0004383116,0.0001272494,0.007204521,0.8112747,0.002846331,0.1767883,0.00001593243],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3670152,0.04264287,0.09363817,0.00156333,0.001773457,0.0002243288,0.0003310716,0.0007887562,0.4920228],"genre_scores_gemma":[0.6357905,0.01024368,0.02192356,0.0002398532,0.00018691,0.00004131091,0.0003917015,0.0002461703,0.3309364],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004944332,"threshold_uncertainty_score":0.01654041,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03926599212203335,"score_gpt":0.2406020312167393,"score_spread":0.201336039094706,"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."}}