{"id":"W7052346815","doi":"","title":"Saskatchewan Commits $80M for Westinghouse eVinci SMR","year":2023,"lang":"en","type":"other","venue":"","topic":"Advanced Electron Microscopy Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Process (computing); Work (physics); Government (linguistics); Plan (archaeology); Scope (computer science)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005626568,0.0002172085,0.0001747807,0.00004446001,0.00005659216,0.00001211786,0.0002321599,0.0003363331,0.0001057169],"category_scores_gemma":[0.0000239497,0.0002107565,0.0001046151,0.00006959742,0.00004375798,5.933402e-7,0.00008139958,0.00008668679,0.00004463971],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001492026,"about_ca_system_score_gemma":0.0000818476,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000155093,"about_ca_topic_score_gemma":0.004056745,"domain_scores_codex":[0.9991261,0.000007848529,0.0001412012,0.0004016863,0.00005140448,0.0002717287],"domain_scores_gemma":[0.9993063,0.00001165196,0.0001077507,0.000486739,0.00003278146,0.00005473743],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000005648644,0.00002046749,0.00001187207,0.0000264439,0.00002980107,2.428537e-7,0.00000267975,0.00000116136,0.09166858,0.000223849,0.9042413,0.003767933],"study_design_scores_gemma":[0.0001321002,0.0001031257,0.000001836702,0.00002888039,0.00001658609,0.000002185616,0.00001549302,0.000003845707,0.09768707,0.0001450237,0.9016273,0.0002365719],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.000169572,0.00186614,0.6196975,0.0004509498,0.0002444568,0.002900028,0.0008992656,0.003046201,0.370726],"genre_scores_gemma":[0.0004508985,0.0004943588,0.0456223,0.0003186675,0.0004728345,0.0003250848,0.0004515998,0.001210886,0.9506534],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.5799274,"threshold_uncertainty_score":0.8594401,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01131080866193687,"score_gpt":0.3355162969147574,"score_spread":0.3242054882528205,"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."}}