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Record W2903465178 · doi:10.2172/1483998

China Vehicle Fleet Model: Estimation of Vehicle Stocks, Usage, Emissions, and Energy Use - Model Description, Technical Documentation, and User Guide

2018· report· en· W2903465178 on OpenAlex
Zifeng Lü, Yan Zhou, Hao Cai, Michael Wang, Xin He, Steven Przesmitzki

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEngineering
TopicVehicle emissions and performance
Canadian institutionsnot available
FundersInternational Institute for Applied Systems AnalysisArgonne National LaboratoryOffice of Energy EfficiencyTsinghua UniversityNational Natural Science Foundation of ChinaOffice of Energy Efficiency and Renewable EnergyU.S. Department of EnergyCanada Excellence Research Chairs, Government of CanadaMassachusetts Institute of Technology
KeywordsChinaUrbanizationIndustrialisationEstimationDocumentationTransport engineeringPopulationPopulation growthEnvironmental scienceKilometerBusinessGeographyAgricultural economicsEngineeringEconomicsComputer scienceEconomic growth

Abstract

fetched live from OpenAlex

With fast economic growth, urbanization, and industrialization, China has experienced rapid expansion in the on-road transportation sector in the past three decades. The highway vehicle population in China increased from 1.65 million in 1980 to 208 million in 2017 with an annual average growth rate (AAGR) of about 14% .

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.544
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.024
GPT teacher head0.284
Teacher spread0.259 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations28
Published2018
Admission routes1
Has abstractyes

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