{"id":"W4402860367","doi":"10.22541/essoar.172736277.74497104/v1","title":"High-resolution national-scale water modeling is enhanced by multiscale differentiable physics-informed machine learning","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Differentiable function; Resolution (logic); Scale (ratio); Computer science; Artificial intelligence; Physics; Mathematics; Mathematical analysis; Quantum mechanics","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","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0003023723,0.000446964,0.0003876152,0.0000464495,0.0002869387,0.0001869017,0.0003891245,0.0003840622,0.005800289],"category_scores_gemma":[0.000043169,0.0003167891,0.0001935897,0.00011995,0.0001324916,0.0001325241,0.002750784,0.001256851,0.003470078],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006565879,"about_ca_system_score_gemma":0.00002065774,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003025008,"about_ca_topic_score_gemma":0.000125771,"domain_scores_codex":[0.9970314,0.00006454334,0.0004817915,0.0009583737,0.0008403228,0.0006235687],"domain_scores_gemma":[0.9994099,0.00004208693,0.0000993089,0.0002826823,0.00002878455,0.0001372733],"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":[0.00001316662,0.00007726262,0.00007721123,0.00006558326,0.00003258404,5.890399e-7,0.0004907206,0.9515344,0.04250119,0.00001740336,0.003980184,0.001209691],"study_design_scores_gemma":[0.0001965313,0.00003205413,0.000009213605,0.00008261918,0.00003670809,8.622616e-7,0.000004302881,0.9217156,0.06109324,0.01544636,0.0009636947,0.0004188103],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9049583,0.00004429672,0.07971181,0.0006497029,0.0004057184,0.0003440036,0.00008780541,0.0005029025,0.01329545],"genre_scores_gemma":[0.968266,0.00002558412,0.006545634,0.0002858915,0.0001288744,0.00007257991,0.0007696099,0.00005701017,0.02384879],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07316617,"threshold_uncertainty_score":0.9999284,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01856162925757925,"score_gpt":0.2444564225296657,"score_spread":0.2258947932720864,"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."}}