{"id":"W3194964709","doi":"10.1007/s10661-021-09351-0","title":"Mobile monitoring and spatial prediction of black carbon in Cairo, Egypt","year":2021,"lang":"en","type":"article","venue":"Environmental Monitoring and Assessment","topic":"Air Quality and Health Impacts","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"International Development Research Centre","keywords":"Mean squared error; Random forest; Statistics; Environmental science; Mean absolute error; Regression; Artificial neural network; Regression analysis; Stepwise regression; Geography; Mathematics; Physical geography; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002396261,0.0001449601,0.0001911555,0.00002805247,0.0000883468,0.00001872899,0.00004985404,0.00008132678,0.00006496565],"category_scores_gemma":[0.000006894242,0.0001550646,0.0000218988,0.00006676117,0.0001639977,0.000136364,0.0002064453,0.0002171039,0.000003267153],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000355128,"about_ca_system_score_gemma":0.00001499623,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001004342,"about_ca_topic_score_gemma":0.00001523351,"domain_scores_codex":[0.9987014,0.00008874261,0.0002986969,0.0003314365,0.0003177417,0.0002620003],"domain_scores_gemma":[0.9995558,0.00004187788,0.00008563959,0.0001591392,0.000001257787,0.000156345],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000008428154,0.0001589847,0.9394379,0.0000375657,0.000006403205,0.00001198009,0.001169596,0.0004371911,0.03208276,0.00000221097,0.00000627071,0.02664073],"study_design_scores_gemma":[0.0004843544,0.0002211851,0.9647428,0.00008266275,0.00001203738,0.000006139347,0.002150759,0.001255371,0.03026119,0.00004417471,0.0006227025,0.0001165555],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9979711,0.0004111116,0.0000619264,0.00008320559,0.0002764116,0.0001772777,0.0000144539,0.00001346231,0.0009910307],"genre_scores_gemma":[0.9967964,0.00153943,0.001332593,0.00001121741,0.0001362283,0.00002752065,0.000005533161,0.00001279087,0.0001383424],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02652417,"threshold_uncertainty_score":0.6323352,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02333709806800354,"score_gpt":0.3001039734490737,"score_spread":0.2767668753810702,"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."}}