{"id":"W2138068562","doi":"10.1002/env.638","title":"Prediction of sea surface temperature from the global historical climatology network data","year":2004,"lang":"en","type":"article","venue":"Environmetrics","topic":"Climate variability and models","field":"Environmental Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Sea surface temperature; Empirical orthogonal functions; Anomaly (physics); Climatology; Environmental science; Special sensor microwave/imager; Mean squared error; Geopotential height; Meteorology; Satellite; Mode (computer interface); Mathematics; Geology; Statistics; Computer science; Microwave; Geography; Precipitation","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":[0.000477955,0.0002862176,0.0001303488,0.0003594279,0.0001354067,0.0002330456,0.0001777938,0.000145049,0.001140687],"category_scores_gemma":[0.001391571,0.0001067952,0.0002204062,0.0005664884,0.00009135146,0.0003004307,0.0001131207,0.0001873819,0.0003196198],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004462462,"about_ca_system_score_gemma":0.0006495463,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02273972,"about_ca_topic_score_gemma":0.02629575,"domain_scores_codex":[0.9998801,0.0000337965,0.000006768594,0.00003637437,0.00003271208,0.00001030423],"domain_scores_gemma":[0.9996576,0.0001099632,0.00004331442,0.00004742148,0.0001265802,0.0000151667],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009418164,0.00005565908,0.1127093,0.00003296698,0.00006442027,0.00004904407,0.00002311179,0.8166865,0.001842835,0.000705097,0.002621669,0.06511513],"study_design_scores_gemma":[0.000008884545,0.00001074075,0.02133303,0.000002844645,0.00000723911,0.000006774215,0.00000941588,0.97679,0.001154228,0.0001970122,0.0004758004,0.000003976052],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9162118,0.0001538122,0.07247651,0.00017212,0.00005698264,0.00005577508,0.005335237,0.0008844776,0.004653239],"genre_scores_gemma":[0.9629717,0.00006827149,0.03158947,0.00001336751,0.00001903672,0.00005404934,0.004266525,0.00004474912,0.0009727788],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02273972,"threshold_uncertainty_score":0.04521471,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04147421846205936,"score_gpt":0.230922740650868,"score_spread":0.1894485221888086,"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."}}