{"id":"W4393595968","doi":"10.5281/zenodo.4641947","title":"Evaluation data for \"Global, high-resolution, reduced-complexity air quality modeling for PM2.5 using InMAP (Intervention Model for Air Pollution)\"","year":2021,"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":"","funders":"","keywords":"Air quality index; Air pollution; Intervention (counseling); Environmental science; Pollution; Meteorology; High resolution; Computer science; Remote sensing; Geography; Psychology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00762678,0.0003790644,0.0004349312,0.0001221867,0.004005229,0.0004466399,0.001876105,0.0002888999,0.0008924849],"category_scores_gemma":[0.005282916,0.0004476118,0.0002323535,0.0004424346,0.0002391252,0.0007292975,0.002737687,0.0003083636,0.0001861669],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002039721,"about_ca_system_score_gemma":0.0000390547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007142826,"about_ca_topic_score_gemma":0.00003451122,"domain_scores_codex":[0.994996,0.0008278397,0.0009660131,0.001405989,0.001097272,0.0007069386],"domain_scores_gemma":[0.9965193,0.00007523018,0.0006245652,0.001550943,0.001018314,0.0002116739],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001485932,0.0002279671,1.915943e-7,0.0003619687,0.00007786124,2.541054e-7,0.00007533242,0.3169462,0.0001837042,0.0002513102,0.6711678,0.01055885],"study_design_scores_gemma":[0.0008251515,0.0001095757,0.00002130491,0.0001289465,0.000198009,0.000009032431,0.0001184018,0.6103076,0.00001948163,0.002143536,0.3857962,0.0003227287],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001315322,0.00005486613,0.4261223,0.0003298584,0.0003059878,0.001615781,0.5700191,0.0001722519,0.00006450505],"genre_scores_gemma":[0.02503744,0.00002365328,0.01781323,0.0001038556,0.0005729831,0.000003313214,0.9557309,0.0005754109,0.0001392409],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.408309,"threshold_uncertainty_score":0.9997976,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3106374137450872,"score_gpt":0.383926445371752,"score_spread":0.07328903162666478,"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."}}