{"id":"W280646573","doi":"10.2166/hydro.2015.072","title":"Assimilation of weather radar and binary ubiquitous sensor measurements for quantitative precipitation estimation","year":2015,"lang":"en","type":"article","venue":"Journal of Hydroinformatics","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Thompson Rivers University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Environmental science; Remote sensing; Radar; Data assimilation; Precipitation; Sensor fusion; Computer science; Wireless sensor network; Weather radar; Mean squared error; Pixel; Meteorology; Geography; Artificial intelligence; Statistics; Mathematics; Telecommunications","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":[0.0008610988,0.0004229402,0.0004226736,0.0005314549,0.0001699646,0.0004877662,0.0006210959,0.0004400314,0.000511379],"category_scores_gemma":[0.002937889,0.000320473,0.0004193542,0.0006641672,0.0002883924,0.00124619,0.0008651551,0.000545095,0.0001287848],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000390255,"about_ca_system_score_gemma":0.0004286326,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007397343,"about_ca_topic_score_gemma":0.00755677,"domain_scores_codex":[0.9996425,0.00009219989,0.00002497635,0.0001003942,0.00009857185,0.00004131858],"domain_scores_gemma":[0.9994382,0.0001773621,0.0001203077,0.0001335348,0.00009400221,0.00003654556],"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":[0.0001654438,0.0001588608,0.01722221,0.00008218223,0.0001103125,0.00007404471,0.00007324189,0.8981612,0.01205925,0.006946718,0.0007287526,0.06421775],"study_design_scores_gemma":[0.000007816444,0.00001651494,0.002658054,0.000002487672,0.000006315321,0.000008377044,0.000009350671,0.9951238,0.0007739051,0.001229583,0.0001573736,0.000006432974],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5300402,0.0002993323,0.4648541,0.0002704961,0.0001274889,0.0000510254,0.0005843882,0.0007573653,0.003015575],"genre_scores_gemma":[0.9744129,0.00006948993,0.02493054,0.00003444492,0.00002094269,0.00001459767,0.0002554677,0.00001093304,0.0002508296],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007397343,"threshold_uncertainty_score":0.01470858,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08633517837545311,"score_gpt":0.2894717058854851,"score_spread":0.203136527510032,"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."}}