{"id":"W4287115850","doi":"10.5281/zenodo.4901479","title":"GEM-MACH, Feedback-DMS","year":2021,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Atmospheric aerosols and clouds","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Mach number; Aerospace engineering; Computer science; Environmental science; Aeronautics; Mechanics; Physics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001133343,0.001571064,0.001499067,0.0005683448,0.0009312787,0.001662369,0.003274409,0.001846948,0.02589342],"category_scores_gemma":[0.001742732,0.001145728,0.001348712,0.00111016,0.0005228748,0.001834062,0.001057958,0.002979265,0.01102407],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00123219,"about_ca_system_score_gemma":0.001812517,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03777291,"about_ca_topic_score_gemma":0.02017621,"domain_scores_codex":[0.9996676,0.0000530213,0.00002086406,0.0001103742,0.00008787992,0.00006027791],"domain_scores_gemma":[0.9992223,0.0001495709,0.00005217399,0.0001773556,0.0002529304,0.0001457522],"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":[0.002221275,0.0006290415,0.01174697,0.000984297,0.0007660158,0.0004493326,0.0003881467,0.4926247,0.01521729,0.01154831,0.4248677,0.03855684],"study_design_scores_gemma":[0.00147622,0.000170811,0.004908417,0.00004191157,0.0001244282,0.00005971236,0.00007150029,0.8747419,0.01711188,0.006482856,0.09468933,0.0001211011],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.135507,0.0006949489,0.07560582,0.001833101,0.002373634,0.0008878675,0.4254979,0.2570871,0.1005126],"genre_scores_gemma":[0.5328472,0.0004041277,0.1211455,0.001171989,0.0003804289,0.0009279769,0.276787,0.03728164,0.02905424],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03777291,"threshold_uncertainty_score":0.08662212,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0186461332239018,"score_gpt":0.2175411776166977,"score_spread":0.1988950443927959,"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."}}