{"id":"W7134827618","doi":"10.2905/jrc.tgrv490","title":"HTAPv3 mosaic: an emission inventory in support to Hemisperic Transport of Air Pollution","year":2022,"lang":"","type":"dataset","venue":"Open MIND","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Air pollution; Context (archaeology); Emission inventory; Mosaic; Air quality index; Pollutant; Air pollutants","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008258011,0.001407146,0.001006332,0.002490667,0.0005679321,0.001681477,0.001978351,0.001361772,0.02150442],"category_scores_gemma":[0.001845581,0.0005840236,0.001387143,0.004886034,0.0002973957,0.001494537,0.001727583,0.00132423,0.01911776],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001220664,"about_ca_system_score_gemma":0.002025406,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0623922,"about_ca_topic_score_gemma":0.06929098,"domain_scores_codex":[0.9994746,0.00007507616,0.00005394589,0.0001524862,0.0001446059,0.00009931275],"domain_scores_gemma":[0.9993159,0.00009831136,0.00008883419,0.0001724173,0.0002278291,0.00009679042],"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":[0.0001435479,0.00006339121,0.006482805,0.0007971489,0.0001424193,0.0001127792,0.0001127865,0.003223703,0.000781631,0.001887688,0.9787886,0.007463435],"study_design_scores_gemma":[0.0002421915,0.00002335538,0.02519151,0.0002986513,0.00006425869,0.00007958093,0.0002777863,0.004819195,0.001340254,0.002682388,0.9648986,0.0000821646],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0006349488,0.00003661444,0.0001981585,0.0000474374,0.00002206031,0.00001343907,0.9979032,0.0004155194,0.0007286211],"genre_scores_gemma":[0.001207765,0.0000348163,0.0006722545,0.00002382044,0.000007827421,0.00005141484,0.9974854,0.00009135544,0.0004253082],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0623922,"threshold_uncertainty_score":0.1240581,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04231338945893352,"score_gpt":0.3324547752093123,"score_spread":0.2901413857503788,"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."}}