{"id":"W2731729575","doi":"10.3390/w9070494","title":"Robot-Assisted Measurement for Hydrologic Understanding in Data Sparse Regions","year":2017,"lang":"en","type":"article","venue":"Water","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Defense Science and Engineering Graduate; Centro Nacional de Investigaciones Cardiovasculares; International Development Research Centre; United States Agency for International Development; National Science Foundation","keywords":"Bathymetry; Workflow; Remote sensing; Environmental science; Computer science; Lidar; Data collection; Sonar; Field (mathematics); Watershed; Elevation (ballistics); Hydrology (agriculture); Artificial intelligence; Geology; Geography; Cartography; Engineering; Database; Machine learning","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.000363809,0.0004655815,0.0003591079,0.0005968928,0.0003169846,0.0006272589,0.0006286839,0.0003948458,0.002472777],"category_scores_gemma":[0.001255929,0.0002677487,0.0002704683,0.0006092129,0.000533573,0.001016132,0.0007500426,0.0004273684,0.0006102334],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003501698,"about_ca_system_score_gemma":0.0007522613,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002725891,"about_ca_topic_score_gemma":0.005172217,"domain_scores_codex":[0.9996933,0.00006277787,0.00001304138,0.00009605871,0.0001136016,0.00002132789],"domain_scores_gemma":[0.9994605,0.0002111292,0.00007699646,0.0001289437,0.0001000604,0.0000223851],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002544429,0.0002688983,0.01463782,0.0004794585,0.00009652607,0.0002961624,0.001236121,0.1826234,0.2969135,0.006607964,0.004511931,0.4920737],"study_design_scores_gemma":[0.00006461712,0.0003142352,0.02060658,0.00004058255,0.00003160723,0.0002446636,0.0005581365,0.8945025,0.05904242,0.008038849,0.01648018,0.00007566548],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07322805,0.00009314757,0.9215356,0.000122844,0.00002673206,0.0001438096,0.0002794159,0.00201049,0.002559992],"genre_scores_gemma":[0.4578419,0.0001243225,0.540121,0.0000660771,0.00002140627,0.000269283,0.000294883,0.00009894838,0.001162269],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002725891,"threshold_uncertainty_score":0.00827229,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3627789194609799,"score_gpt":0.3263942524340957,"score_spread":0.0363846670268842,"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."}}