{"id":"W4393502272","doi":"10.5281/zenodo.5842118","title":"Piecewise Lognormal Approximation Aerosol Model (PAM) data files","year":2022,"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":"Log-normal distribution; Aerosol; Piecewise; Statistical physics; Mathematics; Environmental science; Meteorology; Statistics; Computer science; Geography; Physics; Mathematical analysis","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":[0.0008456873,0.002576148,0.001390404,0.002153188,0.000923087,0.002140078,0.004013399,0.002267764,0.0853596],"category_scores_gemma":[0.003384282,0.0009107834,0.001740442,0.003990855,0.0004403527,0.001767996,0.001480939,0.002149092,0.1297462],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002021246,"about_ca_system_score_gemma":0.002364116,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06949864,"about_ca_topic_score_gemma":0.1024873,"domain_scores_codex":[0.9992403,0.00008411508,0.00006956382,0.0002427886,0.0002313885,0.000131825],"domain_scores_gemma":[0.9982051,0.0002992169,0.0001223024,0.0005479859,0.0006792886,0.0001460175],"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":[0.00003051432,0.00001987528,0.0005426562,0.0001910508,0.00001719724,0.00001459322,0.000007240892,0.0009890159,0.00009637634,0.0003014118,0.9963149,0.001475107],"study_design_scores_gemma":[0.0003363283,0.00002577667,0.005383996,0.0002096863,0.00003236487,0.00008132899,0.00007502495,0.004470796,0.000978591,0.002606654,0.9857416,0.00005787518],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00008693297,0.00001505704,0.00008475187,0.00003229701,0.00001883882,0.00001005915,0.9988899,0.000465234,0.0003969873],"genre_scores_gemma":[0.0003869882,0.00002164307,0.0003550926,0.0000215801,0.000006245818,0.00004596477,0.9984947,0.0001014459,0.0005663207],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0853596,"threshold_uncertainty_score":0.2855563,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0993889738956504,"score_gpt":0.2762207521013803,"score_spread":0.1768317782057299,"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."}}