{"id":"W2526938117","doi":"10.1002/2016jd025568","title":"Using satellite‐based measurements to explore spatiotemporal scales and variability of drivers of new particle formation","year":2016,"lang":"en","type":"article","venue":"Journal of Geophysical Research Atmospheres","topic":"Atmospheric chemistry and aerosols","field":"Earth and Planetary Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"Natural Environment Research Council; Sight Research UK; New York Space Grant Consortium; National Sleep Foundation","keywords":"Satellite; Environmental science; Remote sensing; Particle (ecology); Computer science; Geography; Geology; Aerospace engineering; Engineering; Oceanography","routes":{"ca_aff":true,"ca_fund":false,"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":[0.000447935,0.0001219758,0.0001235733,0.0004435143,0.0001160837,0.0002814415,0.0001644107,0.0001582919,0.0004077974],"category_scores_gemma":[0.0008608478,0.0001381232,0.0001924171,0.0005803131,0.0001476184,0.0003406608,0.0001790618,0.0001457091,0.00007027257],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002791516,"about_ca_system_score_gemma":0.0001247247,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02626009,"about_ca_topic_score_gemma":0.0365903,"domain_scores_codex":[0.9999307,0.00001635828,0.000004178374,0.00002795156,0.00001265261,0.000008091088],"domain_scores_gemma":[0.9993935,0.0002260948,0.0001958769,0.00006497206,0.00008029209,0.00003929687],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002303939,0.00001267027,0.9913747,0.00001063281,0.00006565708,0.00001743054,0.00006289588,0.002621284,0.00257516,0.00005724223,0.00006692499,0.00311249],"study_design_scores_gemma":[0.000001719153,0.000008942938,0.9910007,0.000002411711,0.00000775732,0.000009724859,0.0000441576,0.008471555,0.000288359,0.00005062935,0.0001117112,0.000002214119],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9987929,0.00004454702,0.0004566426,0.00001177802,0.000001384852,0.000003831766,0.000483468,0.0000103514,0.0001950773],"genre_scores_gemma":[0.9992158,0.00002026326,0.0003229817,0.000002335295,0.000001821166,0.00000336687,0.0003860456,0.000001556918,0.00004587806],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02626009,"threshold_uncertainty_score":0.05221444,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1892223805770325,"score_gpt":0.3491576271795175,"score_spread":0.159935246602485,"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."}}