{"id":"W2295361579","doi":"","title":"Exploiting Sentinel 5's Synergy with IRS and 3MI on METOP-SG for Protocol Monitoring and Air Quality-Climate Interaction","year":2012,"lang":"en","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Atmospheric chemistry and aerosols","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Air quality index; Protocol (science); Meteorology; Environmental science; Computer science; Remote sensing; Geology; Geography","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":[0.003450069,0.000509049,0.0002525575,0.0007478469,0.0002998098,0.00076936,0.0006550436,0.0003269204,0.002298419],"category_scores_gemma":[0.001692642,0.0002114091,0.0004855257,0.0007558265,0.0002562304,0.001177269,0.001319869,0.0006913919,0.0008645115],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006372386,"about_ca_system_score_gemma":0.001425807,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0101899,"about_ca_topic_score_gemma":0.01670399,"domain_scores_codex":[0.9988477,0.0002490357,0.00006146073,0.0001961272,0.0004709085,0.0001747],"domain_scores_gemma":[0.9990109,0.00009380685,0.0001614857,0.0003139424,0.0002994619,0.0001203172],"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":[0.001746016,0.0005612826,0.1448878,0.0004468977,0.0006203584,0.0006239546,0.001855133,0.03822482,0.320199,0.01805071,0.07682715,0.3959569],"study_design_scores_gemma":[0.0003770771,0.001554511,0.2275239,0.0003366775,0.0004748076,0.0004533235,0.00186199,0.3004244,0.1216919,0.01168701,0.3332717,0.0003428143],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5995953,0.001405419,0.2645067,0.002165592,0.001427006,0.001293707,0.02506923,0.02056383,0.08397328],"genre_scores_gemma":[0.7478768,0.0003280893,0.2172974,0.0006683875,0.0002046829,0.0003709231,0.02825065,0.0006836494,0.004319405],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0101899,"threshold_uncertainty_score":0.02026117,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02364079275159241,"score_gpt":0.2606073770998047,"score_spread":0.2369665843482123,"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."}}