{"id":"W4405435813","doi":"10.26599/jgse.2024.9280031","title":"Development, hotspots and trend directions of groundwater numerical simulation: A bibliometric and visualization analysis","year":2024,"lang":"en","type":"article","venue":"Journal of Groundwater Science and Engineering","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Visualization; Groundwater; Environmental science; Computer science; Geography; Geology; Data mining; Geotechnical engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":["bibliometrics"],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":["bibliometrics"],"domain":null,"study_design":"design_other","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["bibliometrics","scholarly_communication"],"consensus_categories":["bibliometrics"],"category_scores_codex":[0.004794256,0.0000918813,0.0002237648,0.02454273,0.0001776402,0.001535047,0.0002295871,0.00002225667,0.00001548258],"category_scores_gemma":[0.0004723649,0.00006112262,0.00004285595,0.04523438,0.000115716,0.001383864,0.0001933885,0.00006671055,0.000001214714],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003713783,"about_ca_system_score_gemma":0.00004046709,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001449267,"about_ca_topic_score_gemma":0.00000488364,"domain_scores_codex":[0.9978359,0.0000207771,0.0005674232,0.0003226761,0.001087348,0.0001658493],"domain_scores_gemma":[0.9990291,0.0003246744,0.0001201273,0.0001466845,0.0002498654,0.0001295755],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002752674,0.0001400401,0.03623747,0.0001387713,0.0004486215,0.00004427724,0.008026653,0.08604328,0.005829335,0.0009888996,0.0003866295,0.8616885],"study_design_scores_gemma":[0.0001049869,0.00005984617,0.2279095,0.00004813656,0.00008967939,0.00004562721,0.0002304893,0.756183,0.0001909497,0.000101448,0.01492763,0.0001086729],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6953014,0.0006188996,0.3035789,0.00005822877,0.0003753759,0.0000240183,6.306765e-7,0.00001228553,0.00003037785],"genre_scores_gemma":[0.9959467,0.00006753082,0.003815623,0.000009177704,0.00004110358,5.260131e-7,5.481153e-7,0.000003685929,0.00011512],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8615798,"threshold_uncertainty_score":0.9995015,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07082533311168879,"score_gpt":0.3658895090949396,"score_spread":0.2950641759832507,"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."}}