{"id":"W2112341657","doi":"10.1139/e09-014","title":"Near real-time water vapor distribution surface rendering using Ordinary KrigingThis article is one of a series of papers published in this Special Issue on the theme<i> GEODESY</i>.","year":2009,"lang":"en","type":"article","venue":"Canadian Journal of Earth Sciences","topic":"GNSS positioning and interference","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Changjiang Scholar Program of Chinese Ministry of Education; Natural Sciences and Engineering Research Council of Canada; Li Ka Shing Foundation","keywords":"Kriging; Variogram; Gaussian; Interpolation (computer graphics); Geodesy; Global Positioning System; Geology; Mathematics; Statistics; Algorithm; Applied mathematics; Computer science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005770641,0.00008391488,0.0001714974,0.00009484529,0.0001514834,0.0001167551,0.0002219737,0.00003578043,0.00183954],"category_scores_gemma":[0.00006375865,0.0000573242,0.00004750833,0.0003192346,0.0002408218,0.0004425881,0.000005880464,0.000149219,0.000004220717],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002722917,"about_ca_system_score_gemma":0.0001961454,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002424356,"about_ca_topic_score_gemma":0.001285167,"domain_scores_codex":[0.9990776,0.00003555965,0.0003205253,0.00008795613,0.000206816,0.0002715284],"domain_scores_gemma":[0.9995742,0.00003199697,0.00009034498,0.00009877754,0.00008614801,0.0001185432],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001351487,0.00009869187,0.0148119,0.0000734885,0.00009707492,0.00007195489,0.02215761,0.361522,0.5868126,0.001508428,0.008871851,0.00383931],"study_design_scores_gemma":[0.0005275412,0.001484002,0.03633411,0.001345904,0.00004403494,0.0001366549,0.001675503,0.05192163,0.8958647,0.001335984,0.008879138,0.0004508444],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9848537,0.00004440128,0.00001072921,0.001079825,0.0001731324,0.00004101901,0.00001344843,0.000005114868,0.01377861],"genre_scores_gemma":[0.999252,0.00001947076,0.0004729767,0.00004029035,0.000119193,1.298207e-7,9.628684e-7,0.000003820037,0.00009114108],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3096004,"threshold_uncertainty_score":0.9990729,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01692752622720389,"score_gpt":0.2086714396870627,"score_spread":0.1917439134598588,"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."}}