{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006676911,0.002451686,0.001208399,0.001764848,0.000463914,0.001565542,0.003094671,0.001543042,0.02227179],"category_scores_gemma":[0.002564238,0.0008239382,0.002010763,0.001769099,0.0003303769,0.002154468,0.001358345,0.00142811,0.02291113],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001542046,"about_ca_system_score_gemma":0.001112688,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02266596,"about_ca_topic_score_gemma":0.02927681,"domain_scores_codex":[0.999487,0.00004738373,0.00004319884,0.0001998231,0.0001562024,0.00006622908],"domain_scores_gemma":[0.9993445,0.000140914,0.00004754771,0.000194754,0.0002132274,0.00005900936],"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":[0.0007806752,0.0002282226,0.01244183,0.001128508,0.0004001603,0.0002813262,0.0001067138,0.03514417,0.004182099,0.002733097,0.8997998,0.04277341],"study_design_scores_gemma":[0.0009308548,0.0001968881,0.02077411,0.0003557473,0.0001934083,0.0003290139,0.0001948405,0.2401531,0.01311679,0.01419392,0.7092658,0.0002955612],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.007425356,0.000383118,0.006768397,0.0002278655,0.0001431494,0.000133294,0.9457847,0.03701655,0.002117677],"genre_scores_gemma":[0.01278638,0.0001411293,0.009711793,0.0001643984,0.00002415295,0.0002226416,0.9744506,0.001331221,0.001167701],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02266596,"threshold_uncertainty_score":0.07450652,"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."}}