{"id":"W3044411297","doi":"10.1109/tgrs.2020.3008033","title":"Precipitation Merging Based on the Triple Collocation Method Across Mainland China","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":90,"is_retracted":false,"has_abstract":true,"ca_institutions":"Global Institute for Water Security; University of Saskatchewan","funders":"Global Water Futures; State Key Laboratory of Remote Sensing Science; China Postdoctoral Science Foundation; State Key Laboratory of Resources and Environmental Information System; National Natural Science Foundation of China","keywords":"Mean squared error; Precipitation; Scale (ratio); Computer science; Collocation (remote sensing); Benchmark (surveying); Weighting; Meteorology; Algorithm; Remote sensing; Environmental science; Mathematics; Data mining; Statistics; Machine learning; Geology; Geography; Physics; Geodesy; Cartography","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":[0.0006322131,0.0006286225,0.0005573821,0.001421353,0.000581608,0.0008487226,0.0007546314,0.0004945936,0.001779325],"category_scores_gemma":[0.001550753,0.0003581725,0.0009840674,0.002300796,0.0002815075,0.00111042,0.0009301747,0.0004942933,0.0004644844],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004044212,"about_ca_system_score_gemma":0.001169718,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01518416,"about_ca_topic_score_gemma":0.01560473,"domain_scores_codex":[0.9995185,0.00006369365,0.00003945513,0.0001776547,0.000145778,0.00005503727],"domain_scores_gemma":[0.9995373,0.00006473888,0.0000612482,0.00009858311,0.0001963051,0.00004188755],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005884007,0.0001661948,0.03789833,0.0002727985,0.0003711361,0.0008449497,0.001080038,0.4199667,0.05605007,0.006699639,0.009827388,0.4662344],"study_design_scores_gemma":[0.00005773564,0.00004844158,0.01921229,0.00001422433,0.00008380538,0.00009505246,0.0001755403,0.9599333,0.01233658,0.002041921,0.005946978,0.00005403843],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3813268,0.0003277897,0.6054339,0.0002103977,0.0002374138,0.0002467073,0.00257387,0.003522084,0.006121088],"genre_scores_gemma":[0.6998784,0.0001750669,0.2931542,0.00006477497,0.00005968339,0.0001455692,0.004298018,0.0003118889,0.001912448],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01518416,"threshold_uncertainty_score":0.03019154,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02893915602115757,"score_gpt":0.2613849442538375,"score_spread":0.2324457882326799,"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."}}