{"id":"W4327944080","doi":"10.3390/encyclopedia3010023","title":"A Methodology for Air Temperature Extrema Characterization Pertinent to Improving the Accuracy of Climatological Analyses","year":2023,"lang":"en","type":"article","venue":"Encyclopedia","topic":"Climate variability and models","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"The Scarborough Hospital; University of Toronto","funders":"","keywords":"Maxima and minima; Preprocessor; Metric (unit); Series (stratigraphy); Algorithm; Computer science; Identification (biology); Representation (politics); Mathematics; Artificial intelligence; Geology; Mathematical analysis; Engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.0007844886,0.0001005753,0.0001828974,0.00003539355,0.0001216369,0.00001034832,0.0002213824,0.0000816926,0.0002899402],"category_scores_gemma":[0.001741507,0.00006234812,0.00008190511,0.0003895217,0.00006701171,0.0001018208,0.0002252239,0.00007733727,0.00006541647],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000279865,"about_ca_system_score_gemma":0.00001093756,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006331015,"about_ca_topic_score_gemma":0.00003385027,"domain_scores_codex":[0.9989348,0.0001511912,0.0002667187,0.0002799229,0.0001321977,0.0002351907],"domain_scores_gemma":[0.9986699,0.0009164416,0.00009447249,0.0002509472,0.00001455175,0.00005366054],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00006389955,0.00005588971,0.01697789,0.00003643493,0.000009628368,0.00000151746,0.002174555,0.002507188,0.9696395,0.0001953585,0.0007387095,0.007599474],"study_design_scores_gemma":[0.0005932045,0.0003302887,0.9068862,0.00003084496,0.0001240064,0.00001226278,0.001069436,0.02637153,0.02888802,0.001900006,0.0332972,0.0004969776],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9929232,0.000005742619,0.003499098,0.002414534,0.0001577642,0.0004902919,0.00004085702,0.00005374696,0.0004147906],"genre_scores_gemma":[0.9944846,0.00007363642,0.004277145,0.0005544282,0.00007066184,0.0001585234,0.0000712586,0.00001057344,0.0002991462],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9407514,"threshold_uncertainty_score":0.3174641,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1179700989184119,"score_gpt":0.3635146166681535,"score_spread":0.2455445177497417,"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."}}