{"id":"W2062867593","doi":"10.1007/s00411-011-0368-7","title":"A Monte-Carlo step-by-step simulation code of the non-homogeneous chemistry of the radiolysis of water and aqueous solutions—Part II: calculation of radiolytic yields under different conditions of LET, pH, and temperature","year":2011,"lang":"en","type":"article","venue":"Radiation and Environmental Biophysics","topic":"Advanced oxidation water treatment","field":"Environmental Science","cited_by":36,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Science Council; Canadian Space Agency; Université de Sherbrooke","keywords":"Radiolysis; Aqueous solution; Chemistry; Monte Carlo method; Radiation chemistry; Homogeneous; Diffusion; Chemical kinetics; Proton; Statistical physics; Chemical reaction; Kinetics; Physical chemistry; Physics; Thermodynamics; Nuclear physics; Mathematics; Organic chemistry","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004360583,0.0001337856,0.0002045149,0.00001434712,0.0001100813,0.000002527457,0.00007288508,0.00007788675,0.00005950712],"category_scores_gemma":[0.00000386848,0.00008476756,0.00006687261,0.00006453784,0.0005168411,0.00009782256,0.0001146656,0.00005447257,2.493108e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006965397,"about_ca_system_score_gemma":0.000003734817,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007689241,"about_ca_topic_score_gemma":0.000007084009,"domain_scores_codex":[0.9991589,0.00004230114,0.0003208178,0.0001751873,0.0002044248,0.00009836673],"domain_scores_gemma":[0.9994171,0.00003176085,0.000284278,0.0002249792,0.00000561394,0.00003624263],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002878675,0.0002444637,0.03384863,0.00003969543,0.00006530439,7.986518e-8,0.001116385,0.02832877,0.9354417,0.00001193602,0.00002073161,0.0008535383],"study_design_scores_gemma":[0.0006769543,0.00008302474,0.1965088,0.00002900797,0.000147129,0.000003069628,0.0002565078,0.02072975,0.7813762,0.00006445775,0.00002230722,0.0001027461],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9987887,0.0001390797,0.0004351042,0.00003768098,0.00002311883,0.0002849743,0.0002658918,0.000002626472,0.00002284987],"genre_scores_gemma":[0.9995511,0.0001574969,0.0001063959,0.00001328013,0.000006218006,0.000008560897,0.00005487974,0.000008773026,0.0000932774],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1626602,"threshold_uncertainty_score":0.3456721,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007302132223385466,"score_gpt":0.1840727321090941,"score_spread":0.1767705998857086,"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."}}