{"id":"W2997465055","doi":"10.1088/1748-9326/ab6562","title":"Monitoring hydropower reliability in Malawi with satellite data and machine learning","year":2019,"lang":"en","type":"article","venue":"Environmental Research Letters","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Ministero dell’Istruzione, dell’Università e della Ricerca; Università degli Studi di Milano","keywords":"Hydropower; Environmental science; Climate change; Vulnerability (computing); Scarcity; Reliability (semiconductor); Water scarcity; Environmental resource management; Satellite; Water resources; Computer science; Meteorology; Power (physics); Geography; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007232226,0.0003057602,0.0002111758,0.0009825054,0.0001999158,0.0006661583,0.0003643896,0.0002951132,0.0005469686],"category_scores_gemma":[0.002145204,0.0001215328,0.0002340269,0.001351869,0.0003249671,0.0007274013,0.0006235024,0.0003933012,0.0001095445],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008025611,"about_ca_system_score_gemma":0.0004791358,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0544422,"about_ca_topic_score_gemma":0.05339884,"domain_scores_codex":[0.9997353,0.0001074667,0.0000215786,0.0000442229,0.00004248485,0.00004896481],"domain_scores_gemma":[0.998647,0.0004158482,0.0004194844,0.0001427373,0.0002967461,0.0000780855],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001656817,0.0001124064,0.7151331,0.0001454842,0.000226505,0.0006108815,0.0003110054,0.2316532,0.002935412,0.001271836,0.002232171,0.0452023],"study_design_scores_gemma":[0.00001022992,0.00004983406,0.3562326,0.00006388046,0.00004126285,0.0000751475,0.000583444,0.6377146,0.002205733,0.000877207,0.002119666,0.00002639642],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9913686,0.0002526772,0.004789599,0.0005318881,0.00001235639,0.00001806012,0.001865519,0.00009424046,0.001067007],"genre_scores_gemma":[0.9971061,0.00009322162,0.00167705,0.00001690744,0.00001038122,0.000009818995,0.0009229174,0.000005018918,0.000158679],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0544422,"threshold_uncertainty_score":0.1082506,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02785582173007178,"score_gpt":0.2953377472682203,"score_spread":0.2674819255381485,"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."}}