{"id":"W2963715809","doi":"10.1016/j.nimb.2019.05.010","title":"Modelling chemical kinetics in <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" altimg=\"si1.svg\"> <mml:mrow> <mml:msup> <mml:mrow/> <mml:mrow> <mml:mn>11</mml:mn> </mml:mrow> </mml:msup> <mml:mi mathvariant=\"normal\">C</mml:mi> </mml:mrow> </mml:math> gas target systems towards a generalized production equation","year":2019,"lang":"lv","type":"article","venue":"Nuclear Instruments and Methods in Physics Research Section B Beam Interactions with Materials and Atoms","topic":"Nuclear reactor physics and engineering","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia; TRIUMF","funders":"National Research Council Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Yield (engineering); Kinetics; Saturation (graph theory); Adsorption; Thermodynamics; Chemical kinetics; Kinetic energy; Chemistry; Algorithm; Physics; Mathematics; Physical chemistry; Combinatorics","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":[0.0007497304,0.0009479545,0.001137473,0.0006719704,0.000722375,0.002115676,0.002030243,0.002538224,0.02455687],"category_scores_gemma":[0.002013645,0.0007907912,0.001662255,0.0009385008,0.0008347903,0.002773825,0.001361629,0.001840511,0.00481283],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001402185,"about_ca_system_score_gemma":0.001795378,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0116346,"about_ca_topic_score_gemma":0.009911372,"domain_scores_codex":[0.9996566,0.00008838585,0.00002340063,0.00006398142,0.0001306004,0.00003710313],"domain_scores_gemma":[0.9994503,0.0002626145,0.00006812096,0.00007657351,0.0001091489,0.00003327361],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00004786298,0.00006264911,0.0005285021,0.0003087561,0.00005046076,0.0001844661,0.0001778607,0.6187261,0.007226661,0.3489744,0.01088402,0.01282834],"study_design_scores_gemma":[0.00002449244,0.00001561438,0.0001832655,0.00002647042,0.00001341531,0.00005310628,0.00003013455,0.906521,0.001514306,0.06642886,0.02517059,0.00001870444],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0129006,0.001053959,0.9077068,0.002128377,0.0004963769,0.0001631983,0.003208815,0.001366766,0.07097511],"genre_scores_gemma":[0.3367557,0.003664393,0.4131655,0.001415078,0.0004184672,0.001307228,0.006147345,0.003482965,0.2336434],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02455687,"threshold_uncertainty_score":0.08215094,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03441432799434212,"score_gpt":0.2923649795301845,"score_spread":0.2579506515358423,"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."}}