{"id":"W2277015739","doi":"10.1080/07055900.2015.1135784","title":"Preserving Continuity of Long-Term Daily Maximum and Minimum Temperature Observations with Automation of Reference Climate Stations using Overlapping Data and Meteorological Conditions","year":2016,"lang":"en","type":"article","venue":"ATMOSPHERE-OCEAN","topic":"Climate variability and models","field":"Environmental Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"National Oceanic and Atmospheric Administration; Canadian Meteorological and Oceanographic Society","keywords":"Wind speed; Environmental science; Term (time); Statistics; Series (stratigraphy); Climatology; Matching (statistics); Seasonality; Meteorology; Data set; Mathematics; Geography","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":[],"consensus_categories":[],"category_scores_codex":[0.0003509657,0.0001315133,0.0002243131,0.000005457649,0.0001730341,0.00003169839,0.0002233512,0.00009043168,0.00030324],"category_scores_gemma":[0.0001243102,0.00009194218,0.00001582591,0.000160522,0.0004257123,0.000849934,0.0004757508,0.00007577946,0.00000124644],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003607512,"about_ca_system_score_gemma":0.00002007902,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001634203,"about_ca_topic_score_gemma":0.0003848359,"domain_scores_codex":[0.9987981,0.00008760648,0.0003383856,0.0003831697,0.0001944421,0.0001983506],"domain_scores_gemma":[0.9988619,0.0002914449,0.0002298137,0.0004977466,0.00004485748,0.00007421573],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003624673,0.00008263364,0.8990038,0.00007933503,0.0000261566,0.000001591629,0.0003646182,0.0005767883,0.09875853,0.0006722256,0.00005770229,0.0003404002],"study_design_scores_gemma":[0.0005863908,0.00009592009,0.9824585,0.0001728008,0.00008112981,0.00001048827,0.0001976389,0.01467785,0.0004323597,0.001124486,0.00001780796,0.0001446552],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9964412,0.00002733056,0.002110852,0.0001504247,0.00001572756,0.0002793528,0.0006956087,0.00002815562,0.0002513803],"genre_scores_gemma":[0.9874867,0.00007658284,0.01225289,0.00002701344,0.00000578749,0.000002586632,0.0001079106,0.000008829034,0.00003177161],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09832617,"threshold_uncertainty_score":0.3749293,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04973100817763477,"score_gpt":0.2782580955335325,"score_spread":0.2285270873558978,"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."}}