{"id":"W2229417235","doi":"10.1175/jtech-d-15-0166.1","title":"Sensor-Specific Error Statistics for SST in the Advanced Clear-Sky Processor for Oceans","year":2016,"lang":"en","type":"article","venue":"Journal of Atmospheric and Oceanic Technology","topic":"Meteorological Phenomena and Simulations","field":"Earth and Planetary Sciences","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Environmental science; Daytime; Climatology; Residual; Regression; Meteorology; Mean squared error; Statistics; Computer science; Atmospheric sciences; Mathematics; Algorithm; Geography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001439871,0.0003785258,0.0002744418,0.0005365888,0.0001812013,0.0008204146,0.0005870747,0.0003222767,0.001127838],"category_scores_gemma":[0.006807919,0.0002479834,0.0003721558,0.0009061344,0.0002968292,0.001202285,0.0007779188,0.0006927344,0.0005452205],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005808364,"about_ca_system_score_gemma":0.001116747,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01090991,"about_ca_topic_score_gemma":0.01043729,"domain_scores_codex":[0.9991371,0.0001167587,0.00007243209,0.0002022952,0.0004215181,0.00004994091],"domain_scores_gemma":[0.9984188,0.000381724,0.0001889981,0.0003001239,0.0006735118,0.00003679877],"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":[0.0002649175,0.00007252229,0.09131014,0.0001552296,0.0002133281,0.00009526867,0.0001568326,0.6465251,0.02670971,0.02108958,0.009220725,0.2041866],"study_design_scores_gemma":[0.00003950942,0.00005473458,0.04278779,0.00002740975,0.00002884359,0.00004362773,0.00004029152,0.9171268,0.02078769,0.00522438,0.01378052,0.00005838246],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2558964,0.0002437118,0.7305877,0.0002901586,0.000240826,0.00007657104,0.005164787,0.002704354,0.004795632],"genre_scores_gemma":[0.8038117,0.0001929123,0.1814794,0.000119611,0.00009271785,0.0001409251,0.009884171,0.0009327001,0.003345769],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01090991,"threshold_uncertainty_score":0.02169281,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01846678627302437,"score_gpt":0.2441086459686453,"score_spread":0.2256418596956209,"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."}}