{"id":"W4386393879","doi":"10.5281/zenodo.8310097","title":"OPS particle size distribution, AERONET and Lidar aerosol optical depth data employed in amt-2023-67","year":2023,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Atmospheric aerosols and clouds","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"AERONET; Aerosol; Lidar; Environmental science; Particle-size distribution; Remote sensing; Atmospheric sciences; Particle (ecology); Distribution (mathematics); Particle size; Meteorology; Geography; Geology; Mathematics","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":[0.0009561613,0.001698639,0.001058038,0.002700696,0.0008173461,0.001528562,0.002259624,0.0009763155,0.1678701],"category_scores_gemma":[0.002196965,0.0007350343,0.00150681,0.003387684,0.0002609036,0.001452893,0.001188523,0.001384435,0.1153384],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007441006,"about_ca_system_score_gemma":0.00120205,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02123841,"about_ca_topic_score_gemma":0.01618573,"domain_scores_codex":[0.9994748,0.00002885545,0.00003988152,0.00008408084,0.0002712605,0.0001010924],"domain_scores_gemma":[0.9986355,0.0002083158,0.0000768696,0.000399987,0.0005835733,0.00009578098],"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":[0.0008196644,0.0002732995,0.009807606,0.0006247774,0.0001099552,0.0004204197,0.0001426455,0.004793598,0.006691068,0.001635193,0.9431407,0.03154103],"study_design_scores_gemma":[0.0008147056,0.0002360235,0.04857787,0.0002832779,0.0001408012,0.0004417305,0.0003382484,0.02021165,0.02719851,0.004757314,0.8967881,0.0002118504],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.005519301,0.00003688832,0.002325058,0.00007700456,0.0001578279,0.0001034242,0.9736964,0.008158959,0.009925196],"genre_scores_gemma":[0.01503963,0.00007484953,0.004976814,0.00004824488,0.00005774322,0.0002815591,0.9684772,0.004549783,0.006494137],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1678701,"threshold_uncertainty_score":0.5615815,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03835692572369766,"score_gpt":0.2547874165110723,"score_spread":0.2164304907873746,"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."}}