{"id":"W2614074274","doi":"","title":"How can we alter our carbon footprint? Estimating GHG emissions based on travel survey information","year":2012,"lang":"en","type":"article","venue":"eScholarship@McGill (McGill)","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Greenhouse gas; Carbon footprint; Downtown; TRIPS architecture; Business; Natural resource economics; Beijing; Environmental science; Environmental economics; Geography; Transport engineering; Economics; Engineering; China","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":[0.003465539,0.0006482027,0.0005547006,0.001338803,0.000391132,0.002227278,0.0008514593,0.0009807841,0.003512757],"category_scores_gemma":[0.03040943,0.0003869676,0.0008661205,0.004199948,0.0006339985,0.003749574,0.0006773472,0.001285679,0.001095921],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001415422,"about_ca_system_score_gemma":0.001915865,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1173087,"about_ca_topic_score_gemma":0.158178,"domain_scores_codex":[0.9973117,0.001839963,0.0001002805,0.000233182,0.0003507152,0.0001642355],"domain_scores_gemma":[0.9902942,0.006663482,0.0009177841,0.0008359779,0.001103842,0.0001848229],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002114031,0.0003837803,0.5679433,0.0005076884,0.001892269,0.0001192091,0.0005799496,0.04644104,0.0012589,0.009813731,0.01969256,0.3511561],"study_design_scores_gemma":[0.0001347013,0.000613205,0.5825531,0.001043362,0.002009969,0.0003116904,0.005730463,0.2387844,0.005891932,0.07302602,0.08956387,0.0003372761],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7014485,0.0102521,0.1258496,0.08630349,0.001268453,0.0002369007,0.01752674,0.001148463,0.05596572],"genre_scores_gemma":[0.9388654,0.004983214,0.04727259,0.003417735,0.0001881323,0.00009508822,0.002150669,0.00008856715,0.002938664],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8826913,"threshold_uncertainty_score":0.2332518,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.041120018682833,"score_gpt":0.2821424944978177,"score_spread":0.2410224758149847,"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."}}