{"id":"W4392631055","doi":"10.5194/egusphere-egu24-16210","title":"rechaRge – a package for integrated groundwater recharge modelling in R","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Groundwater recharge; Depression-focused recharge; Groundwater; Hydrology (agriculture); Environmental science; Water resource management; Geology; Aquifer; 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":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003190068,0.002474154,0.001947506,0.001646378,0.0005886153,0.002662888,0.003490926,0.001190527,0.1965293],"category_scores_gemma":[0.0195952,0.001938983,0.002514996,0.00161885,0.0007599388,0.002083185,0.002778208,0.003246235,0.1567984],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006765308,"about_ca_system_score_gemma":0.001929232,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003736331,"about_ca_topic_score_gemma":0.00438831,"domain_scores_codex":[0.9979983,0.0007368731,0.0001675322,0.0004803367,0.0004956617,0.0001213883],"domain_scores_gemma":[0.9948885,0.00306614,0.0003926777,0.0007506267,0.0007322702,0.0001698628],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007657623,0.00001525987,0.0007150638,0.00100996,0.0002559039,0.0001564779,0.0001185689,0.005962046,0.001525043,0.01199546,0.934607,0.04356277],"study_design_scores_gemma":[0.0002664528,0.00004875111,0.001556758,0.0003314385,0.000172144,0.0003256705,0.00003416918,0.02418432,0.004208946,0.04078377,0.9279419,0.0001456627],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.001114019,0.001116484,0.4952299,0.001679889,0.0008310605,0.0002770975,0.1253124,0.3561175,0.01832158],"genre_scores_gemma":[0.02897507,0.001791965,0.4450136,0.002263456,0.0004462182,0.003319725,0.1248911,0.3619412,0.03135759],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.1965293,"threshold_uncertainty_score":0.6574559,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08232699984979554,"score_gpt":0.2923725166310966,"score_spread":0.2100455167813011,"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."}}