{"id":"W4409471616","doi":"10.1016/j.geog.2025.01.005","title":"Refining GNSS-based water storage estimation: Improved hydrological signal extraction using principal component analysis","year":2025,"lang":"en","type":"article","venue":"Geodesy and Geodynamics","topic":"Geophysics and Gravity Measurements","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Center for Strategic Research; Natural Science Foundation of Qinghai; National Natural Science Foundation of China; Ministry of Natural Resources","keywords":"GNSS applications; Principal component analysis; Computer science; Refining (metallurgy); Component (thermodynamics); SIGNAL (programming language); Extraction (chemistry); Estimation; Artificial intelligence; Telecommunications; Global Positioning System; Chemistry; Chromatography; Engineering","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":[0.0003191272,0.0008104719,0.0003531742,0.001309378,0.000235076,0.0004771807,0.0003909524,0.000240667,0.0005326592],"category_scores_gemma":[0.001015729,0.0002894154,0.000551294,0.00164221,0.000186106,0.0007983985,0.0003967677,0.0003896518,0.0003310485],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002404275,"about_ca_system_score_gemma":0.0006396442,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01831483,"about_ca_topic_score_gemma":0.01431047,"domain_scores_codex":[0.9998166,0.00003227962,0.00001324303,0.00004234967,0.00007206203,0.00002341645],"domain_scores_gemma":[0.999816,0.00004582019,0.00002211951,0.00002299258,0.00008598262,0.000007158917],"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":[0.0001596476,0.0001574431,0.02343187,0.0001338303,0.0001576712,0.0002482045,0.0001977194,0.3405693,0.06961405,0.001838826,0.003376812,0.5601146],"study_design_scores_gemma":[0.00001124321,0.0000161369,0.01297639,0.000005210812,0.00002517554,0.00001967799,0.00002630786,0.9790062,0.006294836,0.0005241594,0.00107449,0.00002005659],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3676973,0.0003342335,0.6272455,0.0002417519,0.00005528711,0.00006666005,0.0006328686,0.001926663,0.001799691],"genre_scores_gemma":[0.8132308,0.0003445697,0.1831446,0.00004131082,0.00004375167,0.00005077597,0.00153019,0.0001621926,0.001451921],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01831483,"threshold_uncertainty_score":0.03641647,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01923868470425414,"score_gpt":0.2509515456888901,"score_spread":0.2317128609846359,"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."}}