{"id":"W4393520408","doi":"10.5281/zenodo.4698619","title":"Global Scenarios of Resource and Emission Savings from Material Efficiency in Residential Buildings and Cars","year":2021,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Environmental Impact and Sustainability","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Resource (disambiguation); Resource efficiency; Architectural engineering; Environmental science; Transport engineering; Engineering; Computer science","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":[0.0006341091,0.001117318,0.0006401056,0.001515032,0.000236518,0.0008214238,0.001069786,0.00140627,0.01069222],"category_scores_gemma":[0.001373083,0.000349536,0.001516991,0.002972277,0.0002016649,0.0007543568,0.0006626864,0.0009207161,0.003137146],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009769752,"about_ca_system_score_gemma":0.0005223961,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01598753,"about_ca_topic_score_gemma":0.02321558,"domain_scores_codex":[0.9997399,0.00006502614,0.00001563255,0.00006154168,0.00007021274,0.00004771345],"domain_scores_gemma":[0.9994389,0.0002407438,0.00004496428,0.0001083078,0.0001239905,0.00004306665],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0007143155,0.0002112301,0.01756085,0.001825487,0.0005919038,0.0004187471,0.00009990186,0.5586715,0.001557252,0.009933612,0.3896265,0.01878875],"study_design_scores_gemma":[0.001127938,0.0003806909,0.053599,0.0006494491,0.0003061704,0.0005201646,0.0005356277,0.2591009,0.005989888,0.02227774,0.6552446,0.0002678431],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.04492373,0.0003805562,0.001430699,0.0003628179,0.00008721736,0.00004066015,0.9393789,0.001111139,0.01228427],"genre_scores_gemma":[0.1042828,0.0003757117,0.003294947,0.0001296376,0.00002668282,0.00014143,0.889442,0.0003769042,0.001929891],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01598753,"threshold_uncertainty_score":0.03576899,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008504293155186207,"score_gpt":0.2240316714977691,"score_spread":0.2155273783425829,"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."}}