{"id":"W4389949721","doi":"10.3390/app14010012","title":"Predicting River Discharge in the Niger River Basin: A Deep Learning Approach","year":2023,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Hydropower; Environmental science; Hydroelectricity; Hydrology (agriculture); Drainage basin; Discharge; Dry season; Streamflow; Percentile; Structural basin; Water resource management; Geography; Geology; Ecology; Cartography","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":[],"consensus_categories":[],"category_scores_codex":[0.001718989,0.0001146214,0.00009685796,0.00007704789,0.0005850987,0.0000983545,0.0006265026,0.00002942864,0.0001817743],"category_scores_gemma":[0.00001569704,0.00007194732,0.00003244752,0.001367059,0.0006315694,0.0002434866,0.0003654372,0.0001719413,0.0006795367],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003575972,"about_ca_system_score_gemma":0.000005475673,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003434299,"about_ca_topic_score_gemma":0.0001007519,"domain_scores_codex":[0.998283,0.0000715503,0.0001445663,0.0004168832,0.0006575334,0.0004264478],"domain_scores_gemma":[0.9996657,0.00008935777,0.00005689892,0.0001502261,0.000001488268,0.00003636271],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000007147401,0.0001349217,0.8812169,0.00001292248,0.000009994857,0.000008755661,0.02634908,0.05974065,0.0007476319,0.009293217,0.004013231,0.01846551],"study_design_scores_gemma":[0.0003763964,0.00005879112,0.8444453,0.000007224815,0.00001519879,0.000001567793,0.01580024,0.1225107,0.00008106975,0.003105946,0.0132969,0.0003005548],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8339161,0.000009135625,0.0007911469,0.000390631,0.00006901689,0.000347877,3.921209e-7,0.00008680084,0.164389],"genre_scores_gemma":[0.9971282,0.00002413978,0.001752572,0.0002991671,0.00003543556,0.000102483,0.000004346658,0.000006072341,0.0006476235],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1637413,"threshold_uncertainty_score":0.8734295,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01589230736108352,"score_gpt":0.2375475944871754,"score_spread":0.2216552871260919,"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."}}