{"id":"W4393663054","doi":"10.5281/zenodo.7732338","title":"HETEAC – The Hybrid End-To-End Aerosol Classification model for EarthCARE: Look-Up Table (LUT) for aerosol mixtures","year":2023,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Air Quality Monitoring and Forecasting","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Aerosol; Table (database); Lookup table; Environmental science; Computer science; Remote sensing; Meteorology; Geography; Data mining","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":[0.001651657,0.0003535663,0.0002969372,0.0001266125,0.003913871,0.0009001127,0.001957603,0.0001762048,0.001611513],"category_scores_gemma":[0.00171479,0.0003201078,0.0001504629,0.0004899782,0.0003023236,0.0002402,0.001712068,0.00050375,0.01159351],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003690193,"about_ca_system_score_gemma":0.00001061843,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002072692,"about_ca_topic_score_gemma":0.00001136767,"domain_scores_codex":[0.9967557,0.000268308,0.0004898607,0.0009616812,0.0007475263,0.0007769385],"domain_scores_gemma":[0.9979855,0.0001879903,0.000291928,0.001055669,0.0002131858,0.0002656925],"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":[0.00008653252,0.00005764108,4.210445e-7,0.0001235382,0.00003670108,0.000001447868,0.0003116946,0.002556833,0.001696736,0.00004454802,0.9773152,0.01776865],"study_design_scores_gemma":[0.0003650321,0.0002264472,0.00001654126,0.00005921994,0.00005354403,0.00001496407,0.0001753859,0.02102155,0.0006105619,0.00009983235,0.9769834,0.0003734769],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0005857334,0.0000357901,0.02292627,0.001581043,0.0006355378,0.002719003,0.9699928,0.000652756,0.000871102],"genre_scores_gemma":[0.0069354,0.00009864377,0.000803074,0.0003226499,0.0007375865,0.000007288746,0.9807054,0.001739408,0.008650557],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0221232,"threshold_uncertainty_score":0.9999251,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08281725330213349,"score_gpt":0.2837521665343812,"score_spread":0.2009349132322477,"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."}}