{"id":"W7095776226","doi":"","title":"5.5 IMPACT OF GROUND-BASED GPS OBSERVATIONS ON THE CANADIAN REGIONAL ANALYSIS AND FORECAST SYSTEM","year":2015,"lang":"en","type":"article","venue":"","topic":"Physiological and biochemical adaptations","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Global Positioning System; Troposphere; Moisture; Field (mathematics); Time series","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":[0.003960449,0.0009050093,0.0006616237,0.001264354,0.00175359,0.003192979,0.001268017,0.0010489,0.006818929],"category_scores_gemma":[0.0143744,0.0003837394,0.0009532761,0.002120524,0.0006509636,0.001449433,0.001265667,0.00121885,0.0007029243],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01222819,"about_ca_system_score_gemma":0.01951539,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9884143,"about_ca_topic_score_gemma":0.9808171,"domain_scores_codex":[0.9972988,0.000504379,0.0001553359,0.000368328,0.001048766,0.0006243382],"domain_scores_gemma":[0.9911348,0.001723152,0.0005039571,0.0003419038,0.005747475,0.000548724],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008965679,0.0001373919,0.6340067,0.0003093111,0.0008475257,0.0003849071,0.0002833765,0.2763034,0.004621895,0.003329174,0.03166445,0.04721534],"study_design_scores_gemma":[0.00008492597,0.00008091221,0.7738486,0.0001830956,0.0002705594,0.0000355243,0.0006509072,0.2077648,0.002252036,0.0006109825,0.01409572,0.0001218443],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8988042,0.002864393,0.003128722,0.01366759,0.0004958707,0.0001259716,0.04261163,0.0009810929,0.03732051],"genre_scores_gemma":[0.9805886,0.0007533228,0.002273467,0.0005910277,0.00003992941,0.00001315193,0.01187613,0.0001008246,0.003763487],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01222819,"threshold_uncertainty_score":0.08872217,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1042697106094031,"score_gpt":0.2489185128333836,"score_spread":0.1446488022239805,"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."}}