{"id":"W1964106924","doi":"10.1002/qua.22134","title":"Improving reptation quantum Monte Carlo","year":2009,"lang":"en","type":"article","venue":"International Journal of Quantum Chemistry","topic":"Quantum, superfluid, helium dynamics","field":"Physics and Astronomy","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Brock University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reptation; Quantum Monte Carlo; Monte Carlo method; Statistical physics; Hamiltonian (control theory); Electron; Diffusion Monte Carlo; Node (physics); Hybrid Monte Carlo; Physics; Population; Quantum; Mathematics; Quantum mechanics; Mathematical optimization; Markov chain Monte Carlo; Statistics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002964924,0.0002155936,0.0002689724,0.00008890015,0.00006107393,0.0001031153,0.0006376004,0.00007539303,0.0002774134],"category_scores_gemma":[0.00008163983,0.000210721,0.000293061,0.00009937408,0.00004510511,0.0004600397,0.00004672781,0.0004495217,0.00001677081],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001525601,"about_ca_system_score_gemma":0.0002063891,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001042054,"about_ca_topic_score_gemma":4.716871e-7,"domain_scores_codex":[0.9980444,0.00002633591,0.0007913103,0.0002137075,0.0006766804,0.0002475375],"domain_scores_gemma":[0.9981306,0.00007120875,0.0006798728,0.0002072938,0.0007670152,0.0001440623],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004994221,0.000897285,0.01797915,0.00004860296,0.0007324121,0.0003017795,0.001000922,0.00469784,0.8832159,0.03160835,0.0036719,0.0553464],"study_design_scores_gemma":[0.004796567,0.000399778,0.01163947,0.0005538021,0.0002319491,0.0005855134,0.002448298,0.7723739,0.1690811,0.03331261,0.003322855,0.00125418],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.971531,0.0001790086,0.02518516,0.001098122,0.0009132118,0.00004999071,0.00006557695,0.00002406342,0.0009538366],"genre_scores_gemma":[0.9973453,0.00001246708,0.0005419673,0.0001201658,0.001613408,0.000001746681,0.00002191936,0.00002176424,0.0003213183],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7676761,"threshold_uncertainty_score":0.8592955,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006435594610609676,"score_gpt":0.2531320184901277,"score_spread":0.2466964238795181,"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."}}