{"id":"W2072150591","doi":"10.1063/1.4707848","title":"Antiparticle plasmas for antihydrogen trapping","year":2012,"lang":"en","type":"article","venue":"AIP conference proceedings","topic":"Atomic and Molecular Physics","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; York University; TRIUMF; University of Calgary; Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Financiadora de Estudos e Projetos; Israel Science Foundation; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Engineering and Physical Sciences Research Council; TRIUMF; U.S. Department of Energy; National Science Foundation","keywords":"Antihydrogen; Antiproton; Physics; Antiparticle; Nuclear physics; Antimatter; Large Hadron Collider; Atomic physics; Plasma; Trapping; Positron; Atom (system on chip); Particle physics; Proton; Electron; Lepton; 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":[0.0002295811,0.00040251,0.00030498,0.0003479613,0.0003427705,0.0007149179,0.0005084019,0.0007922759,0.005593722],"category_scores_gemma":[0.0002292117,0.000218256,0.0002010889,0.000242183,0.0004296862,0.001040531,0.0007036984,0.001069368,0.002036363],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004050026,"about_ca_system_score_gemma":0.0001413469,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001282596,"about_ca_topic_score_gemma":0.000209497,"domain_scores_codex":[0.9998572,0.00002175215,0.000005644143,0.00003786685,0.00005857336,0.00001907314],"domain_scores_gemma":[0.9999187,0.00002972545,0.0000167557,0.000009604184,0.00001605992,0.000009021555],"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":[0.0001467361,0.00007033536,0.0003623565,0.001301302,0.00003646035,0.0003466417,0.0002681848,0.0009522843,0.7632226,0.1538424,0.005978887,0.07347182],"study_design_scores_gemma":[0.00006221024,0.0005968234,0.0008694776,0.0001861734,0.00004334753,0.001964866,0.0001550749,0.007547028,0.5446486,0.03117892,0.4126841,0.00006349291],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.2050449,0.3431148,0.300914,0.003652399,0.003934971,0.0004126648,0.0006402239,0.001427161,0.1408588],"genre_scores_gemma":[0.7859124,0.07724734,0.08723509,0.001373325,0.000763465,0.0003502378,0.000566763,0.0002545137,0.04629685],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005593722,"threshold_uncertainty_score":0.01871282,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0260211733771508,"score_gpt":0.2605657858349358,"score_spread":0.234544612457785,"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."}}