{"id":"W4393545464","doi":"10.5281/zenodo.4507058","title":"Supplimentary Data: Representativity of Cloud-Profiling Radar Observations for Data Assimilation in Numerical Weather Prediction","year":2021,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Meteorological Phenomena and Simulations","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Data assimilation; Profiling (computer programming); Meteorology; Environmental science; Cloud computing; Radar; Numerical weather prediction; Weather radar; Computer science; Geography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001457693,0.001013463,0.00075763,0.001828234,0.0005620276,0.002000531,0.001740152,0.001485812,0.1572989],"category_scores_gemma":[0.007602678,0.0004368479,0.0007915274,0.004020005,0.0003199546,0.001571008,0.001086414,0.001319194,0.05591059],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000916057,"about_ca_system_score_gemma":0.001886455,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02835819,"about_ca_topic_score_gemma":0.02533657,"domain_scores_codex":[0.9989335,0.0001174416,0.0001352348,0.0002359106,0.0004567002,0.0001211737],"domain_scores_gemma":[0.9923472,0.001213994,0.000525484,0.001748705,0.003877116,0.0002874753],"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":[0.0003854781,0.0001630498,0.0145014,0.0006645313,0.0001414892,0.000195573,0.0000754342,0.00412403,0.001746006,0.001732731,0.9520558,0.02421454],"study_design_scores_gemma":[0.0003589652,0.00008091376,0.05564082,0.0002951342,0.00007522697,0.0001989185,0.0002904857,0.007722064,0.003802737,0.002734106,0.9287079,0.00009271785],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.003036686,0.00006372794,0.001055737,0.0002093133,0.0004946271,0.00005637525,0.9898338,0.0006591401,0.004590513],"genre_scores_gemma":[0.02060938,0.0001240639,0.003656049,0.0001713162,0.000162163,0.0001507163,0.9697828,0.0005689654,0.004774475],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1572989,"threshold_uncertainty_score":0.5262173,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1816538507551733,"score_gpt":0.3054908508676038,"score_spread":0.1238370001124305,"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."}}