{"id":"W4402035159","doi":"10.32920/26882509.v1","title":"Life Cycle Effects of Mobility Technologies and Services","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Vehicle emissions and performance","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; Toronto Metropolitan University","funders":"","keywords":"Subsidy; Economic shortage; Electricity; Environmental science; Lithium (medication); Business; Natural resource economics; Pollution; Environmental economics; Engineering; Economics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0007700998,0.0005963271,0.0002066783,0.001223533,0.0004161968,0.001198443,0.000552011,0.0005351357,0.0105258],"category_scores_gemma":[0.003060091,0.0002479645,0.0009673744,0.001186008,0.000423657,0.001216156,0.000671217,0.0004439931,0.0007647379],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004327691,"about_ca_system_score_gemma":0.002063618,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03716113,"about_ca_topic_score_gemma":0.02790724,"domain_scores_codex":[0.9992371,0.0001795125,0.00002400234,0.0000702291,0.0002562598,0.0002330003],"domain_scores_gemma":[0.9987215,0.0005044307,0.0001721341,0.00006978033,0.0004498811,0.00008210252],"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":[0.0005536679,0.0002091392,0.02475934,0.0003537372,0.0001747957,0.0005177296,0.0001651054,0.7875309,0.008239402,0.06715771,0.006455553,0.1038829],"study_design_scores_gemma":[0.00007882825,0.001156631,0.05656061,0.000174178,0.0003055813,0.0004828185,0.001376133,0.786266,0.01851667,0.04757462,0.0873854,0.0001224573],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7974332,0.003549493,0.02872568,0.001737095,0.0001471296,0.0004567743,0.01086316,0.0001737397,0.1569137],"genre_scores_gemma":[0.9766988,0.001240512,0.002422315,0.00008575781,0.0000118752,0.00007482369,0.001590263,0.00002868103,0.01784693],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03716113,"threshold_uncertainty_score":0.07388967,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004458100537718578,"score_gpt":0.2141843569686656,"score_spread":0.209726256430947,"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."}}