{"id":"W4327980587","doi":"10.3389/feart.2023.1150740","title":"The application of Monte Carlo modelling to quantify in situ hydrogen and associated element production in the deep subsurface","year":2023,"lang":"en","type":"article","venue":"Frontiers in Earth Science","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University of Ottawa","funders":"Nuclear Waste Management Organization; Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Biosphere; Monte Carlo method; Environmental science; Earth science; Hydrogen production; Computer science; Geology; Hydrogen; Statistics; Mathematics; Physics","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":[0.001125421,0.0004403715,0.0004341535,0.0007302817,0.0004903488,0.001019313,0.0007252535,0.001472109,0.0008385557],"category_scores_gemma":[0.00375434,0.000584631,0.0007191141,0.0006123655,0.0006271582,0.0007964699,0.0004845502,0.0006270566,0.0001539848],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001143031,"about_ca_system_score_gemma":0.001080155,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01840176,"about_ca_topic_score_gemma":0.01407284,"domain_scores_codex":[0.9997039,0.0001440172,0.0000182507,0.00004724176,0.00006159196,0.0000248687],"domain_scores_gemma":[0.9976495,0.001855539,0.0002046029,0.0001310728,0.000120731,0.00003862749],"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":[0.00001010933,0.000008199201,0.001130414,0.000009904084,0.0000126242,0.000009053273,0.00001088918,0.995293,0.0004581138,0.001316007,0.00003427883,0.001707399],"study_design_scores_gemma":[0.000001256635,0.00000454936,0.0001865708,0.000001837953,0.000002267043,0.00000468763,0.000003023844,0.998677,0.0002232905,0.0007950661,0.00009659376,0.000003897245],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2038385,0.0003578837,0.7886756,0.0003348581,0.00004375538,0.00009392175,0.0003833804,0.000524444,0.00574763],"genre_scores_gemma":[0.9061232,0.0002501948,0.09202106,0.00006779865,0.00002041842,0.0001518571,0.0001833749,0.00005547133,0.001126643],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01840176,"threshold_uncertainty_score":0.03658926,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008296689202525845,"score_gpt":0.2122375266679488,"score_spread":0.203940837465423,"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."}}