{"id":"W4399803839","doi":"10.4204/eptcs.403.16","title":"Uniform Sampling and Visualization of 3D Reluctant Walks","year":2024,"lang":"en","type":"article","venue":"Electronic Proceedings in Theoretical Computer Science","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Orthant; Random walk; Sampling (signal processing); Sample (material); Visualization; Mathematics; Statistical physics; Combinatorics; Computer science; Statistics; Data mining; Physics; Computer vision","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":[0.0007717954,0.0005012942,0.0005016886,0.001004343,0.0004555679,0.001564506,0.000617705,0.0007537201,0.003422561],"category_scores_gemma":[0.005117219,0.0003403114,0.0004690485,0.0007129666,0.0006968732,0.001043547,0.00138964,0.0007653397,0.0007738785],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004076648,"about_ca_system_score_gemma":0.0004175209,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001503066,"about_ca_topic_score_gemma":0.002204105,"domain_scores_codex":[0.9995484,0.0001778928,0.00001912396,0.0001020154,0.0001098079,0.00004273048],"domain_scores_gemma":[0.998576,0.0007955721,0.0001065016,0.0002274299,0.0001845411,0.0001099413],"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":[0.000900875,0.0001512601,0.01206741,0.0005210704,0.00009389195,0.001176789,0.002786987,0.4130867,0.07360816,0.3270702,0.01115074,0.1573859],"study_design_scores_gemma":[0.00003804092,0.00004812057,0.00157769,0.00004785158,0.000007051373,0.0002988392,0.0001297836,0.9198863,0.006288467,0.06420808,0.00743993,0.00002977248],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09809335,0.0004443318,0.893356,0.0002398719,0.00005856144,0.00005470268,0.0005092528,0.002168756,0.005075179],"genre_scores_gemma":[0.7352693,0.0003854356,0.259341,0.000180627,0.00004334784,0.0001515942,0.0009063518,0.0008282924,0.002893975],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003422561,"threshold_uncertainty_score":0.01144958,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01021162179528529,"score_gpt":0.2927010076531216,"score_spread":0.2824893858578363,"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."}}