{"id":"W6941170516","doi":"10.13021/jssr2023.4003","title":"Improving Accuracy in PM2.5 Interpolation Using AI and ML","year":2023,"lang":"en","type":"article","venue":"George Mason University","topic":"Mycorrhizal Fungi and Plant Interactions","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Interpolation (computer graphics); Multivariate interpolation; Weighting; Mean squared error; Grid; Inverse distance weighting","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":[],"consensus_categories":[],"category_scores_codex":[0.00008564298,0.00004966909,0.00005693452,0.00003333002,0.0001350105,0.0000241218,0.00006159706,0.00004300895,0.0005687051],"category_scores_gemma":[0.00006752183,0.00002625861,0.00002122221,0.0003415511,0.000003856127,0.0002305567,0.00007848364,0.00008684707,0.00006070409],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002416382,"about_ca_system_score_gemma":0.00000370406,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006518615,"about_ca_topic_score_gemma":0.001428624,"domain_scores_codex":[0.9996135,0.00003412317,0.0000501156,0.0001314888,0.00004433297,0.0001264312],"domain_scores_gemma":[0.9995444,0.0003537728,0.00002954685,0.00002007277,0.00001558453,0.00003663495],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001127728,0.00003254027,0.5658156,0.00001421069,0.000008264808,0.00007537659,0.0003198949,0.00008316626,0.3834984,0.002294933,0.001994119,0.04575075],"study_design_scores_gemma":[0.0003200329,0.0001784379,0.5990859,0.0001768278,0.00004065971,0.00005645184,0.006600313,0.2178392,0.001244723,0.0009476257,0.1728386,0.0006711939],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9982897,0.00001347643,0.00002793582,0.0003984309,0.00009987062,0.00006050451,0.000169346,0.00005222619,0.0008885005],"genre_scores_gemma":[0.9991618,0.0000751746,0.00003870127,0.00004632725,0.00003631638,8.328517e-8,0.0001982182,2.936741e-7,0.0004431219],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3822536,"threshold_uncertainty_score":0.9854239,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0154005034622334,"score_gpt":0.2078299860967463,"score_spread":0.1924294826345129,"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."}}