{"id":"W2080005693","doi":"10.1063/1.2927878","title":"A Monte Carlo algorithm to study polymer translocation through nanopores. II. Scaling laws","year":2008,"lang":"en","type":"article","venue":"The Journal of Chemical Physics","topic":"Nanopore and Nanochannel Transport Studies","field":"Engineering","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Monte Carlo method; Statistical physics; Scaling; Exponent; Nanopore; Scaling law; Diffusion; Polymer; Physics; Monte Carlo algorithm; Function (biology); Materials science; Mathematics; Thermodynamics; Nanotechnology; Statistics","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":[0.001334145,0.0005424041,0.0005364267,0.0008574067,0.0006171016,0.0006646877,0.0009254431,0.001051271,0.002073621],"category_scores_gemma":[0.005238983,0.0004366606,0.0004776956,0.0008662348,0.0008044646,0.001014107,0.00063383,0.0008978175,0.0004846448],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009385787,"about_ca_system_score_gemma":0.001025095,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003590759,"about_ca_topic_score_gemma":0.004251834,"domain_scores_codex":[0.9996798,0.0001331269,0.00002183206,0.00003571177,0.0001034847,0.00002617398],"domain_scores_gemma":[0.9978862,0.001578561,0.00009053316,0.0001414337,0.000233841,0.00006938864],"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":[0.0000348471,0.00006520418,0.0009881625,0.00007647133,0.00004697291,0.00006050793,0.00007495634,0.8480572,0.001863855,0.1078809,0.001344174,0.03950681],"study_design_scores_gemma":[0.000004618004,0.000004692696,0.00003157543,0.000003076453,0.000001555118,0.000006447388,0.000001415138,0.9913585,0.0001517551,0.007797894,0.000636177,0.000002269458],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01067081,0.0003509441,0.9857447,0.0001590308,0.00005749495,0.0001090194,0.00005262199,0.0004312857,0.002424105],"genre_scores_gemma":[0.1706559,0.0005302425,0.8240123,0.0001579073,0.00006852826,0.001042983,0.0001609569,0.0002760093,0.003095108],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003590759,"threshold_uncertainty_score":0.007139742,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01903387206014292,"score_gpt":0.2273332541905478,"score_spread":0.2082993821304049,"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."}}