{"id":"W4383648114","doi":"10.2139/ssrn.4504411","title":"How Reliable are Hybrid Ai-Based Models Compared to Numerical Model for Predicting Long-Term Horizon Groundwater Level Under Changing Climate?","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Term (time); Horizon; Groundwater; Environmental science; Econometrics; Computer science; Economics; Mathematics; Geology; Geotechnical engineering; Physics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.002973478,0.000685537,0.0007859771,0.0002767568,0.0009188353,0.0005136401,0.001091643,0.0003491683,0.00002588379],"category_scores_gemma":[0.0001184629,0.0006168219,0.0004379471,0.0003307432,0.0001125799,0.0003554082,0.001414735,0.003968857,0.00007078658],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004839955,"about_ca_system_score_gemma":0.000524544,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001225543,"about_ca_topic_score_gemma":0.000480367,"domain_scores_codex":[0.9909282,0.000121209,0.0006689838,0.001228443,0.0009070287,0.006146142],"domain_scores_gemma":[0.9982915,0.0001094268,0.0005700684,0.0005662087,0.00007404271,0.0003887619],"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":[0.0001733344,0.00012135,0.003721725,0.00006359057,0.00009248241,0.00001569841,0.0001087183,0.9936913,0.0001606821,0.0002953684,0.0001687076,0.001387013],"study_design_scores_gemma":[0.0006328403,0.0003963978,0.0003545639,0.0003152416,0.0001102195,0.000104206,0.00007607509,0.9067245,0.0001153123,0.09051707,0.00001195514,0.0006416318],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3860821,0.0000658832,0.6098522,0.002723735,0.0003823196,0.0005926735,0.0000624688,0.0002146585,0.00002393505],"genre_scores_gemma":[0.9913763,0.0001015144,0.005488222,0.0005449564,0.000417436,0.0001702409,0.0001240244,0.0001995717,0.001577674],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6052942,"threshold_uncertainty_score":0.9996283,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07655254906301043,"score_gpt":0.282172961496871,"score_spread":0.2056204124338606,"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."}}