{"id":"W4393420840","doi":"10.5281/zenodo.8187835","title":"Supporting Dataset for \"Reducing a tropical cyclone weak-intensity bias in a global numerical weather prediction system\"","year":2023,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Tropical and Extratropical Cyclones Research","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Tropical cyclone; Tropical cyclone forecast model; Meteorology; Intensity (physics); Environmental science; Numerical weather prediction; Climatology; Geography; Geology; Physics","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":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0007493342,0.0003400388,0.0005422603,0.0002895244,0.001365775,0.0008102094,0.001372836,0.0003157271,0.003832667],"category_scores_gemma":[0.001703271,0.0003070827,0.0001451268,0.00102898,0.0002073853,0.0002361963,0.0005660089,0.0008157604,0.01617737],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001797769,"about_ca_system_score_gemma":0.0000210831,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006883706,"about_ca_topic_score_gemma":0.0003315437,"domain_scores_codex":[0.9957623,0.0005711275,0.0007417649,0.001000627,0.0008392565,0.001084949],"domain_scores_gemma":[0.9982521,0.0001454718,0.0002089097,0.0006412557,0.0002797505,0.0004724999],"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":[0.0003594209,0.00009911637,0.0007621525,0.0004431319,0.00005285047,0.0001031806,0.00002434804,0.0002422007,0.000005884746,0.000042487,0.9845192,0.01334596],"study_design_scores_gemma":[0.0006308058,0.0004841434,0.04243415,0.000137767,0.00003786566,0.000180425,0.0001716768,0.004855694,0.000001438644,0.00005770597,0.9507257,0.0002825985],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.002574754,0.00006106278,0.00108959,0.0004490609,0.0004016077,0.0009283504,0.9935591,0.0004284146,0.0005080755],"genre_scores_gemma":[0.02240509,0.00007475085,0.0001501787,0.0000659482,0.0005181998,2.509285e-7,0.9764884,0.0002270939,0.00007007703],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.041672,"threshold_uncertainty_score":0.9999381,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05172711813486088,"score_gpt":0.2816056632933777,"score_spread":0.2298785451585168,"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."}}