{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","insufficient_payload"],"category_scores_codex":[0.0003364918,0.001729316,0.001545429,0.0007529869,0.0003141673,0.0005885215,0.00136678,0.001620791,0.006110445],"category_scores_gemma":[0.0001022253,0.001816189,0.0007717006,0.0007314752,0.00009780264,0.0006168885,0.0001194963,0.002087415,0.04161717],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004640648,"about_ca_system_score_gemma":0.0002924713,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003457189,"about_ca_topic_score_gemma":0.00005241649,"domain_scores_codex":[0.9942729,0.0001396128,0.001512212,0.001507788,0.000978091,0.001589417],"domain_scores_gemma":[0.9967206,0.0002612054,0.0003845234,0.001710271,0.0004537225,0.0004697396],"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":[0.001299669,0.002239833,0.003209744,0.01899092,0.006852293,0.0002265658,0.02541459,0.05492337,0.09077664,0.1240292,0.5745853,0.09745189],"study_design_scores_gemma":[0.01013213,0.004486586,0.02307336,0.02197075,0.004407628,0.0005303736,0.02336067,0.4347366,0.2339625,0.006869663,0.2099564,0.02651333],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.09070089,0.002169919,0.003150576,0.0001082855,0.01334831,0.00107591,0.0001516354,0.001600209,0.8876942],"genre_scores_gemma":[0.7364766,0.0008059455,0.000839903,0.000210541,0.0005158574,0.0000995559,0.002188369,0.0004193466,0.2584439],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.6457757,"threshold_uncertainty_score":0.9996753,"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."}}