{"id":"W4413640718","doi":"10.1016/j.asr.2025.08.055","title":"Scale-factor-based regional zenith tropospheric delay modeling for real-time precise point positioning","year":2025,"lang":"en","type":"article","venue":"Advances in Space Research","topic":"GNSS positioning and interference","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China; Ministry of Natural Resources","keywords":"Zenith; Troposphere; Scale factor (cosmology); Precise Point Positioning; Scale (ratio); Environmental science; Geodesy; Remote sensing; Meteorology; Point (geometry); Computer science; Geology; Global Positioning System; GNSS applications; Physics; Mathematics; Astronomy; Telecommunications","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004108247,0.0001552847,0.0002035432,0.000264199,0.0001951489,0.00008482994,0.000255864,0.00009291036,0.00003792904],"category_scores_gemma":[0.00009708397,0.0001461037,0.00006694027,0.0006172432,0.00009055807,0.0003767367,0.00003955659,0.0003978311,0.00002419956],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003776223,"about_ca_system_score_gemma":0.00009854513,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007319367,"about_ca_topic_score_gemma":0.00006082706,"domain_scores_codex":[0.998477,0.00008898063,0.0002472407,0.0003166001,0.0003170967,0.000553065],"domain_scores_gemma":[0.9989748,0.0004079233,0.00001877121,0.0002669618,0.0002537857,0.00007777966],"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":[0.0002216572,0.00006922653,0.0004476551,0.0002414731,0.00001886466,0.000004157422,0.0002776553,0.9733327,0.01408315,0.002232749,0.00102274,0.008047969],"study_design_scores_gemma":[0.0005609926,0.0001267079,0.00009598006,0.0008634754,0.000004806088,0.000002358287,0.0001921581,0.9862709,0.006394374,0.004290219,0.001017056,0.0001809762],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.276042,0.004460561,0.6585475,0.001394543,0.0003313863,0.001064461,0.00003683223,0.0004961001,0.05762665],"genre_scores_gemma":[0.9743047,0.0007644483,0.02372137,0.0000157045,0.0000516137,0.0002173723,0.00002529697,0.00003432995,0.0008651249],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6982628,"threshold_uncertainty_score":0.5957936,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0266224950261312,"score_gpt":0.3473277240009583,"score_spread":0.3207052289748271,"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."}}