{"id":"W4386791694","doi":"10.58837/chula.the.2019.1275","title":"การประมาณคาปรมาณไอนำในบรรยากาศในทนทดวยการประมวลผลขอมลจเอนเอสเอส แบบจดเดยวความละเอยดสงในประเทศไทย","year":2019,"lang":"th","type":"dissertation","venue":"","topic":"GNSS positioning and interference","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Nowcasting; Zenith; GNSS applications; Meteorology; Precise Point Positioning; Troposphere; Environmental science; Precipitable water; Numerical weather prediction; Weather forecasting; Geodesy; Geography; Global Positioning System; Water vapor; Computer science; Telecommunications","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":[0.0007915828,0.0006432554,0.0002631801,0.0009072345,0.001214206,0.003373785,0.0006405801,0.000689728,0.2092607],"category_scores_gemma":[0.001764279,0.0002550789,0.000334394,0.001065768,0.0005543232,0.001864427,0.001152145,0.001249342,0.2050042],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001891115,"about_ca_system_score_gemma":0.002281351,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00642458,"about_ca_topic_score_gemma":0.008367778,"domain_scores_codex":[0.9993762,0.00008886906,0.00002635154,0.000148271,0.0002871638,0.00007324369],"domain_scores_gemma":[0.9992703,0.00005685628,0.00003433923,0.0001015956,0.0004625829,0.00007446724],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006347636,0.00004985586,0.0007314942,0.0003480054,0.00001403002,0.00007738694,0.000660705,0.0005696176,0.001793275,0.08783254,0.5230045,0.3848551],"study_design_scores_gemma":[0.00000359768,0.00000858568,0.0004260975,0.00008175424,0.000002483584,0.00003757674,0.0001226184,0.0001033577,0.0005759691,0.004741474,0.9938904,0.000006000862],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.002270809,0.002762988,0.01407189,0.004379587,0.001973502,0.0002107775,0.00432785,0.001294351,0.9687082],"genre_scores_gemma":[0.01987874,0.005068239,0.01595107,0.001381612,0.0006131028,0.0002151955,0.005451123,0.0005512584,0.9508897],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.2092607,"threshold_uncertainty_score":0.7000469,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006772202394738213,"score_gpt":0.220366985889489,"score_spread":0.2135947834947508,"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."}}