{"id":"W4248142232","doi":"10.5194/amtd-7-7569-2014","title":"SPARTAN: a global network to evaluate and enhance satellite-based estimates of ground-level particulate matter for global health applications","year":2014,"lang":"en","type":"preprint","venue":"","topic":"Air Quality and Health Impacts","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; University of Toronto; Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Indian Institute of Technology Kanpur; United States Agency for International Development","keywords":"Nephelometer; Environmental science; Satellite; Particulates; Remote sensing; Meteorology; Aerosol; Geography; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.005246078,0.001092825,0.0005415448,0.00300384,0.0003570619,0.0009320901,0.001316222,0.0005266221,0.00561406],"category_scores_gemma":[0.003460673,0.0003629537,0.0003242676,0.003404104,0.0002504538,0.001265131,0.00183107,0.0003913189,0.002540758],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009272692,"about_ca_system_score_gemma":0.003009742,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01999162,"about_ca_topic_score_gemma":0.0162123,"domain_scores_codex":[0.9988709,0.0003766853,0.00007056569,0.0002292464,0.0003542963,0.00009832196],"domain_scores_gemma":[0.9944656,0.0005581058,0.000786457,0.0007753814,0.002632537,0.0007818911],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001504232,0.0006609531,0.2309973,0.001082207,0.0006164885,0.0002848807,0.0003969487,0.03250264,0.01429868,0.005712909,0.3254384,0.3865043],"study_design_scores_gemma":[0.001026894,0.001232172,0.3185466,0.0004893023,0.0004434797,0.0002663075,0.0005605521,0.1244676,0.01017163,0.004038878,0.5386211,0.0001355865],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.2550574,0.002817333,0.1315943,0.002577086,0.0007267798,0.00662819,0.5071238,0.02207646,0.07139868],"genre_scores_gemma":[0.2318217,0.00129716,0.2092729,0.0006837935,0.0001950595,0.004269003,0.5361316,0.001406071,0.01492288],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01999162,"threshold_uncertainty_score":0.03975046,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06471048600448234,"score_gpt":0.3966958837698701,"score_spread":0.3319853977653878,"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."}}