{"id":"W2330127336","doi":"10.1103/physrevc.67.064316","title":"Atomic mass determinations for<mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" display=\"inline\"><mml:mrow><mml:msup><mml:mrow/><mml:mrow><mml:mn>183</mml:mn></mml:mrow></mml:msup></mml:mrow><mml:mi mathvariant=\"normal\">W</mml:mi></mml:math>and<mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" display=\"inline\"><mml:mrow><mml:msup><mml:mrow/><mml:mrow><mml:mn>199</mml:mn></mml:mrow></mml:msup></mml:mrow><mml:mi mathvariant=\"normal\">Hg</mml:mi></mml:math>and the mercury problem","year":2003,"lang":"lv","type":"article","venue":"Physical Review C","topic":"Mass Spectrometry Techniques and Applications","field":"Chemistry","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Safety Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Physics; Table (database); Atomic mass; Analytical Chemistry (journal); Atomic physics; Data mining; Chemistry; Computer science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication","open_science","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","sts","research_integrity","insufficient_payload"],"category_scores_codex":[0.004912035,0.002877929,0.001300609,0.001087166,0.004566435,0.00407618,0.006027898,0.005275433,0.4515872],"category_scores_gemma":[0.004736726,0.005002575,0.005788574,0.003363606,0.004876277,0.003806293,0.005171407,0.005256487,0.00442116],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000694561,"about_ca_system_score_gemma":0.003873812,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002207404,"about_ca_topic_score_gemma":0.00134157,"domain_scores_codex":[0.9765808,0.001263165,0.005316424,0.005008762,0.005634782,0.006196047],"domain_scores_gemma":[0.9783705,0.005138214,0.006039532,0.00681514,0.0005946629,0.003041982],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00186151,0.001111973,0.00001455797,0.006975012,0.003325053,0.001907297,0.001935471,0.0005758861,0.004532429,0.7970065,0.1774501,0.003304194],"study_design_scores_gemma":[0.004071406,0.002298991,0.0001061022,0.004739184,0.006210051,0.005296555,0.002003816,0.2375188,0.7103034,0.0004541678,0.02285399,0.004143585],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3526595,0.006712481,0.002793061,0.002899224,0.00277897,0.0002494633,0.002201755,0.0008392842,0.6288663],"genre_scores_gemma":[0.9503676,0.01467917,0.008670194,0.003281235,0.004646328,0.009520106,0.005229943,0.002452831,0.001152557],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7965524,"threshold_uncertainty_score":0.99935,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01552982201229172,"score_gpt":0.2563255061001151,"score_spread":0.2407956840878234,"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."}}