{"id":"W4310595455","doi":"10.1021/acs.est.2c05255","title":"Material Stock and Embodied Greenhouse Gas Emissions of Global and Urban Road Pavement","year":2022,"lang":"en","type":"article","venue":"Environmental Science & Technology","topic":"Environmental Impact and Sustainability","field":"Environmental Science","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Norges Forskningsråd; Norges Teknisk-Naturvitenskapelige Universitet","keywords":"Stock (firearms); Per capita; Greenhouse gas; Agricultural economics; Environmental science; Population; Natural resource economics; Geography; Business; Environmental engineering; Economics; Ecology","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":"codex-gemma-dda1882f352a","candidate_categories":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000460009,0.000220005,0.0002310242,0.00009320723,0.0008475828,0.00002224938,0.0005337389,0.00007699963,0.002648674],"category_scores_gemma":[0.00002221886,0.0002122691,0.00003493853,0.000555901,0.005235164,0.00027246,0.003628397,0.000188994,0.00001271007],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001327469,"about_ca_system_score_gemma":0.00001910885,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000233318,"about_ca_topic_score_gemma":0.00001527763,"domain_scores_codex":[0.9977507,0.00005745624,0.0003038746,0.0006895645,0.0006307774,0.0005676561],"domain_scores_gemma":[0.9991761,0.00000941232,0.0001367491,0.0004511988,9.451972e-7,0.0002256305],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003087271,0.0003263909,0.8092633,0.000004135762,0.000005153309,0.0000121919,0.0003031046,0.00006669296,0.1726712,0.0003028226,0.00008816606,0.01692599],"study_design_scores_gemma":[0.0008929982,0.001500056,0.948937,0.000004953496,0.00003233193,0.0001788589,0.005118367,0.0004321245,0.03227573,0.004079789,0.006042519,0.0005052493],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9977106,0.0001003129,0.00001637541,0.0004600926,0.00008114631,0.0004281702,0.00009871794,0.00005585241,0.001048715],"genre_scores_gemma":[0.9991695,0.00004732087,0.0004590809,0.00008872287,0.000007365287,0.00005952759,0.000005253344,0.00001119941,0.000151988],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1403954,"threshold_uncertainty_score":0.9982631,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004734432232015029,"score_gpt":0.2199257629637424,"score_spread":0.2151913307317274,"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."}}