{"id":"W2623709367","doi":"","title":"Combining remote sensing and hydrological models to increase spatial and temporal resolution","year":2000,"lang":"en","type":"other","venue":"CGSPace A Repository of Agricultural Research Outputs (Consultative Group for International Agricultural Research)","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Remote sensing; Environmental science; Commission; Geography; Hydrology (agriculture); Geology; Political science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001430082,0.0006017938,0.0005582814,0.001010699,0.000192519,0.001089584,0.0005909515,0.0005975831,0.003568162],"category_scores_gemma":[0.003291642,0.0004515897,0.0007954282,0.001556199,0.0001598749,0.002233183,0.000915684,0.0007938921,0.0012243],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000389345,"about_ca_system_score_gemma":0.0004979835,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01432971,"about_ca_topic_score_gemma":0.0277335,"domain_scores_codex":[0.9997208,0.00008870778,0.00001408913,0.00007551355,0.00007906186,0.00002194411],"domain_scores_gemma":[0.9991692,0.0003626093,0.00005528153,0.0002043316,0.0001800227,0.00002861548],"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":[0.0002573973,0.0006474493,0.02145623,0.0003501582,0.0007175425,0.0001648746,0.0002649952,0.4809534,0.01987395,0.005136271,0.02501791,0.4451599],"study_design_scores_gemma":[0.0001911374,0.00007850241,0.009371103,0.00003966243,0.0002275298,0.00005732366,0.00006764969,0.9623365,0.005710148,0.00725848,0.01459446,0.00006737998],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3585052,0.004362064,0.561487,0.004978599,0.001163194,0.0003745108,0.01281315,0.0200719,0.03624438],"genre_scores_gemma":[0.5942694,0.001649605,0.3876245,0.0006124219,0.0002903587,0.0001875678,0.008907285,0.001048349,0.005410582],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01432971,"threshold_uncertainty_score":0.02849257,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04633577340754323,"score_gpt":0.308841072722187,"score_spread":0.2625052993146438,"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."}}