{"id":"W4393726814","doi":"10.5281/zenodo.3520885","title":"A cross-checked global monthly weather station database for precipitation covering the period 1901 to 2010","year":2019,"lang":"en","type":"dataset","venue":"Figshare","topic":"Advanced Computational Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Period (music); Precipitation; Database; Climatology; Environmental science; Meteorology; Geography; Computer science; Geology","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00008114386,0.0002405616,0.0001717417,0.00005142867,0.0002393958,0.0004766117,0.00142781,0.0001259122,0.001400917],"category_scores_gemma":[0.0003912256,0.0002052468,0.00009629026,0.0003292975,0.000007737604,0.0004852725,0.0004969583,0.0001670011,0.001074326],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001502999,"about_ca_system_score_gemma":0.0001540397,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002718161,"about_ca_topic_score_gemma":0.00003421998,"domain_scores_codex":[0.9985263,0.00002416866,0.0002730637,0.0005936223,0.0003283396,0.000254495],"domain_scores_gemma":[0.9980972,0.0002095796,0.0002571347,0.001030596,0.0003278277,0.00007762642],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000007944534,0.00002157787,3.636153e-7,0.00009198939,0.000009835338,8.054477e-7,0.0000315018,0.006267558,0.000003678934,0.0001986073,0.9913939,0.001972236],"study_design_scores_gemma":[0.0001510525,0.00006366149,0.0002756165,0.0003124463,0.000007548257,0.000003104793,0.000006146526,0.01370396,0.0000277178,0.00105139,0.9841388,0.0002585126],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000002132515,0.00004696695,0.1153348,0.000358547,0.0001376113,0.001665642,0.8823024,0.0001006844,0.00005120534],"genre_scores_gemma":[0.00002778178,0.000002136785,0.03512474,0.000621586,0.0001495769,0.0030675,0.9608446,0.00001486808,0.000147221],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08021005,"threshold_uncertainty_score":0.9997035,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05468343849926022,"score_gpt":0.3618500508620456,"score_spread":0.3071666123627854,"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."}}