{"id":"W6929131559","doi":"10.48550/arxiv.1203.2915","title":"A New Monte Carlo Method for Time-Dependent Neutrino Radiation Transport","year":2012,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Perimeter Institute","funders":"","keywords":"Monte Carlo method; Monte Carlo method for photon transport; Monte Carlo molecular modeling; Neutrino; Dynamic Monte Carlo method; Radiation transport; Hybrid Monte Carlo; Monte Carlo method in statistical physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009074022,0.0005073613,0.0006403237,0.0006197651,0.0007667811,0.0008904677,0.001709596,0.001238732,0.003439142],"category_scores_gemma":[0.00308241,0.0004498739,0.0008233944,0.0007633447,0.0009617007,0.001316931,0.0009546776,0.001574989,0.0007167113],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001092884,"about_ca_system_score_gemma":0.002073564,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007708789,"about_ca_topic_score_gemma":0.007444066,"domain_scores_codex":[0.9996811,0.00009718576,0.00001895191,0.00004273852,0.0001318245,0.00002827202],"domain_scores_gemma":[0.9988821,0.0005956749,0.00007617551,0.0001318564,0.0002410989,0.0000730599],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005376817,0.00005409586,0.0005812914,0.00008292788,0.00003696084,0.0001033036,0.00008537478,0.806662,0.006523617,0.151583,0.001283275,0.03295042],"study_design_scores_gemma":[0.000009157818,0.000004130205,0.00002610081,0.000004010545,0.000002954929,0.00001372156,0.000001985689,0.9914564,0.0004312141,0.006259958,0.001785104,0.000005264035],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003501529,0.00009466503,0.9938558,0.00008539722,0.00006281908,0.00003804049,0.000054981,0.0001955015,0.002111262],"genre_scores_gemma":[0.1222733,0.0002835896,0.8704097,0.0001346406,0.00007231435,0.0003181686,0.0002063278,0.0003024437,0.005999692],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007708789,"threshold_uncertainty_score":0.01532781,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02668604857270904,"score_gpt":0.194975036589975,"score_spread":0.168288988017266,"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."}}