{"id":"W2375049792","doi":"","title":"Temporal and spatial distribution and long-term variation trend of precipitation in Xi'an","year":2010,"lang":"en","type":"article","venue":"Ganhanqu ziyuan yu huanjing","topic":"Environmental Changes in China","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Science North","funders":"","keywords":"Precipitation; Spatial distribution; Environmental science; Climatology; Spatial variability; Wet season; Spring (device); Seasonality; Period (music); Trend analysis; Atmospheric sciences; Geography; Geology; Meteorology; Ecology; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000290833,0.000139432,0.0001372795,0.0007999376,0.0002407429,0.0002942962,0.0001802432,0.000119382,0.0004940856],"category_scores_gemma":[0.0003153879,0.0001381881,0.000139453,0.001476865,0.0001447915,0.0001624375,0.0002381368,0.0001483422,0.00005467435],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006749385,"about_ca_system_score_gemma":0.0004320196,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02996149,"about_ca_topic_score_gemma":0.06443438,"domain_scores_codex":[0.9998903,0.00001280094,0.00001180546,0.0000346845,0.00002734705,0.00002306778],"domain_scores_gemma":[0.9997699,0.00003980141,0.00008516016,0.0000140932,0.00005848631,0.00003252103],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000604919,0.00002305285,0.9851409,0.00005119487,0.0000917047,0.0002037709,0.0005599115,0.00210537,0.002118772,0.0002156166,0.0003535547,0.009075472],"study_design_scores_gemma":[9.224559e-7,0.000007342753,0.9983894,0.000002948092,0.000007192372,0.00001817215,0.0001055959,0.0009926828,0.00008126187,0.00001654956,0.0003754998,0.000002469023],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9988446,0.0001047969,0.0001372361,0.00002850472,0.000002663008,0.00000269499,0.0004326584,0.000005460929,0.0004414402],"genre_scores_gemma":[0.998647,0.00009362024,0.0001474753,0.000005184754,0.000003955013,0.000007133465,0.0008113029,0.000001107278,0.0002832616],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02996149,"threshold_uncertainty_score":0.05957419,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01025348722464309,"score_gpt":0.2452405478974397,"score_spread":0.2349870606727966,"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."}}