{"id":"W4297795858","doi":"10.15485/1463822","title":"Scaling factors to improve the temporal and spatial distribution of CO2 emissions from global fossil fuel emission datasets","year":2013,"lang":"en","type":"dataset","venue":"OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information)","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Scaling; Environmental science; Fossil fuel; Distribution (mathematics); Spatial distribution; Atmospheric sciences; Climatology; Geography; Geology; Remote sensing; Mathematics; Engineering; Waste management","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":[0.003695719,0.002828789,0.0015037,0.005120453,0.001057487,0.002265213,0.002307025,0.001541375,0.01692373],"category_scores_gemma":[0.01628829,0.0007585236,0.004386585,0.007956148,0.0007215656,0.002288659,0.002540386,0.004070404,0.0185986],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001088625,"about_ca_system_score_gemma":0.001652455,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01949864,"about_ca_topic_score_gemma":0.03642663,"domain_scores_codex":[0.9970006,0.0005357479,0.0003890299,0.001167283,0.0006123523,0.0002949753],"domain_scores_gemma":[0.9971705,0.0006901587,0.0001948691,0.001124877,0.0006781928,0.0001415285],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005046841,0.0004434239,0.01963863,0.001880677,0.0008089138,0.0001857953,0.0002400136,0.01181971,0.002858307,0.003110121,0.9157188,0.04279098],"study_design_scores_gemma":[0.001626142,0.000153982,0.07107624,0.0004162632,0.0003713468,0.000344266,0.0003374235,0.0275528,0.006583211,0.009950406,0.8813097,0.0002781985],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.01154902,0.0005803545,0.005415751,0.0002923744,0.0004453779,0.0002494363,0.9703872,0.008569893,0.002510593],"genre_scores_gemma":[0.01000777,0.0001611669,0.01281235,0.0001014679,0.00005628265,0.0005709874,0.9740365,0.001174173,0.001079169],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01949864,"threshold_uncertainty_score":0.05661553,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007420253354293108,"score_gpt":0.2162210443380728,"score_spread":0.2088007909837797,"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."}}