{"id":"W3081939977","doi":"10.3390/data5030075","title":"High-Resolution Surface Water Classifications of the Xingu River, Brazil, Pre and Post Operationalization of the Belo Monte Hydropower Complex","year":2020,"lang":"en","type":"article","venue":"Data","topic":"Fish biology, ecology, and behavior","field":"Environmental Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; McGill University","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Natural Sciences and Engineering Research Council of Canada; DigitalGlobe Foundation","keywords":"Operationalization; Hydropower; Land cover; Remote sensing; Geography; Surface water; Pixel; Hydrology (agriculture); Environmental science; Cartography; Land use; Geology; Ecology; Computer science","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002589085,0.0003238074,0.0002387645,0.001684282,0.0002909528,0.0004697111,0.0003064884,0.0002108099,0.001289339],"category_scores_gemma":[0.000626897,0.0001663356,0.0002603442,0.001514256,0.0002475445,0.0003614704,0.0006492445,0.0002195057,0.0004238086],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008260175,"about_ca_system_score_gemma":0.0005453398,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1165565,"about_ca_topic_score_gemma":0.1880905,"domain_scores_codex":[0.9997346,0.0000309953,0.00002505309,0.00009781583,0.00005737297,0.0000542136],"domain_scores_gemma":[0.9996938,0.0000359403,0.00007302553,0.00006889433,0.00009203524,0.00003643122],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000335527,0.0003789787,0.8371913,0.0002449702,0.0001541621,0.0007017822,0.002032722,0.0232402,0.01365434,0.001711767,0.02323218,0.097122],"study_design_scores_gemma":[0.00002300466,0.00002533833,0.9717422,0.00003032908,0.00002139931,0.0001067789,0.0006221957,0.0105723,0.001086752,0.000154315,0.0155906,0.00002470904],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9383078,0.0001159549,0.001277742,0.0001865204,0.00002050976,0.00005827229,0.0561907,0.0002423335,0.003600111],"genre_scores_gemma":[0.8731028,0.0001445363,0.004824793,0.00003785641,0.00001652385,0.0001324838,0.1201814,0.00005151379,0.001508001],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1165565,"threshold_uncertainty_score":0.2317562,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04060715776044358,"score_gpt":0.2676884213395465,"score_spread":0.2270812635791029,"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."}}