{"id":"W7033711111","doi":"","title":"Search for D&lt;sup&gt;0&lt;/sup&gt;-DÌ&lt;sup&gt;0&lt;/sup&gt;Mixing and a Measurement of the Doubly Cabibbo-Suppressed Decay Rate in D&lt;sup&gt;0&lt;/sup&gt;â KÏ Decays","year":2003,"lang":"en","type":"article","venue":"eScholarship (California Digital Library)","topic":"Radar Systems and Signal Processing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institut National de Physique Nucléaire et de Physique des Particules; Institute of High Energy Physics; Natural Sciences and Engineering Research Council of Canada; Centre National de la Recherche Scientifique; Bundesministerium für Bildung und Forschung; SLAC National Accelerator Laboratory; Alexander von Humboldt-Stiftung; Alfred P. Sloan Foundation; Deutsche Forschungsgemeinschaft; U.S. Department of Energy; National Science Foundation","keywords":"Mixing (physics); Detector; Limit (mathematics); Asymmetry; Particle decay; Measure (data warehouse)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008604619,0.0005849835,0.0003927519,0.001462753,0.000753509,0.00100383,0.0006336129,0.0007734852,0.003656749],"category_scores_gemma":[0.0009617314,0.0004681614,0.0001924046,0.001067896,0.0002904257,0.0004172143,0.0005568264,0.000446121,0.0007835765],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002525696,"about_ca_system_score_gemma":0.0003181784,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004451896,"about_ca_topic_score_gemma":0.0008636799,"domain_scores_codex":[0.9996144,0.00006083336,0.00001963864,0.0001339902,0.00009604142,0.00007511832],"domain_scores_gemma":[0.9992731,0.0002159585,0.000245496,0.00006265121,0.00007632301,0.0001263557],"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":[0.004056744,0.0004106942,0.1258312,0.0003202901,0.0002004208,0.001693057,0.0003725156,0.001891143,0.796157,0.02108588,0.002216852,0.04576415],"study_design_scores_gemma":[0.0006838516,0.001300876,0.08846529,0.00006616808,0.0003022996,0.004078881,0.0003687087,0.02922414,0.8494272,0.0105719,0.01539664,0.0001141688],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9826927,0.0005395436,0.006281565,0.0002218857,0.00001432762,0.00002077726,0.0004778364,0.0002401166,0.009511176],"genre_scores_gemma":[0.9939231,0.00009413145,0.004337226,0.00007158631,0.00001022054,0.00001334551,0.0005067786,0.00002152401,0.001022072],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003656749,"threshold_uncertainty_score":0.01223302,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02083952441531824,"score_gpt":0.2105246168147657,"score_spread":0.1896850923994475,"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."}}