{"id":"W2971359022","doi":"","title":"Quantifying Asian Power Plant CO 2 Emissions from Space","year":2019,"lang":"en","type":"article","venue":"99th American Meteorological Society Annual Meeting","topic":"Spacecraft and Cryogenic Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Environmental science; Space (punctuation); Greenhouse gas; Computer science; Geology","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":[0.0005634331,0.0008482893,0.0002450459,0.0005782535,0.0004390424,0.001076772,0.0003019109,0.0003828714,0.0008255057],"category_scores_gemma":[0.0003773872,0.0001671307,0.0005265998,0.00106474,0.0002236768,0.001163494,0.0005407513,0.0003677134,0.0001820202],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007596815,"about_ca_system_score_gemma":0.0008207877,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0274503,"about_ca_topic_score_gemma":0.028619,"domain_scores_codex":[0.9998676,0.00001942073,0.000006451816,0.00003551065,0.00003940707,0.00003161206],"domain_scores_gemma":[0.9998422,0.00004566361,0.0000170181,0.0000202292,0.00006092143,0.00001393182],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.001657453,0.0003183844,0.2683412,0.0004046344,0.0008364888,0.001052552,0.0006706394,0.5171283,0.1192871,0.006891832,0.002926217,0.08048512],"study_design_scores_gemma":[0.0001180399,0.0007037952,0.271309,0.00004810182,0.0007627329,0.0001865183,0.001745598,0.5372916,0.1760628,0.003035092,0.008631763,0.0001049903],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9842582,0.0001552914,0.006091354,0.00009306706,0.00001976285,0.00001978696,0.0007538256,0.00009902516,0.008509839],"genre_scores_gemma":[0.9976471,0.00006183216,0.001166744,0.00001325186,0.000003722085,0.00001058614,0.0004319368,0.00001910616,0.0006457582],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0274503,"threshold_uncertainty_score":0.05458105,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01214645895829273,"score_gpt":0.2393616087038231,"score_spread":0.2272151497455304,"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."}}