{"id":"W2019144722","doi":"10.1103/physrevlett.96.070601","title":"Worm Algorithm for Continuous-Space Path Integral Monte Carlo Simulations","year":2006,"lang":"en","type":"article","venue":"Physical Review Letters","topic":"Quantum, superfluid, helium dynamics","field":"Physics and Astronomy","cited_by":403,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Path integral Monte Carlo; Monte Carlo method; Path integral formulation; Computation; Diagonal; Statistical physics; Hybrid Monte Carlo; Lattice (music); Dynamic Monte Carlo method; Computer science; Monte Carlo molecular modeling; Algorithm; Physics; Space (punctuation); Monte Carlo method in statistical physics; Path (computing); Quantum Monte Carlo; Markov chain Monte Carlo; Mathematics; Quantum mechanics; Quantum; Geometry","routes":{"ca_aff":true,"ca_fund":false,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000103403,0.0002979451,0.0005678046,0.00003208603,0.0001182505,0.00005466403,0.0002216375,0.00001279168,0.00004277509],"category_scores_gemma":[0.00001188549,0.000260947,0.0004597705,0.0002134997,0.00007424733,0.000182602,0.00004862721,0.0002270158,0.00006098579],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005661179,"about_ca_system_score_gemma":0.00002834562,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004828711,"about_ca_topic_score_gemma":0.000009702414,"domain_scores_codex":[0.9985715,0.00006185898,0.0003499988,0.0003679338,0.0002130129,0.0004357639],"domain_scores_gemma":[0.999092,0.0002421383,0.000124116,0.000357842,0.00009360839,0.00009023314],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004547052,0.003280002,0.02362775,0.002205879,0.0008527131,0.00002177026,0.0007784573,0.01914091,0.02836757,0.1397845,0.286576,0.4953189],"study_design_scores_gemma":[0.001306764,0.0001075663,0.001116792,0.001308981,0.0004874018,0.000001082265,0.00005402126,0.8960606,0.0002696654,0.005319332,0.09293485,0.001032933],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6809944,0.003924075,0.299486,0.01009923,0.0004321996,0.002761529,0.001114096,0.000204445,0.0009840621],"genre_scores_gemma":[0.9905888,0.00004877845,0.004812656,0.00257769,0.001313098,0.000197818,0.0002341376,0.00006252916,0.0001644639],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8769197,"threshold_uncertainty_score":0.9999843,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007849604312827217,"score_gpt":0.2667561180495871,"score_spread":0.2589065137367599,"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."}}