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Record W2534959367 · doi:10.11159/icte16.106

Study on the Beijing Transportation Energy Saving and Consumption Reduction under the Total Energy Consumption Restriction

2016· article· en· W2534959367 on OpenAlexvenueno aff
Hu Hong, Runzhuo Wang, Yanfen Tang

Bibliographic record

VenueProceedings of the World Congress on Civil, Structural, and Environmental Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicVehicle emissions and performance
Canadian institutionsnot available
Fundersnot available
KeywordsEnergy consumptionBeijingConsumption (sociology)Reduction (mathematics)Total energyEnergy (signal processing)Computer scienceEnvironmental economicsEnvironmental scienceEconomicsEngineeringElectrical engineeringChinaGeographyStatisticsMathematics

Abstract

fetched live from OpenAlex

Total energy consumption and intensity in transportation industry are the critical factors to the success of future energy safety and greenhouse gas emission control. Total energy consumption in aviation transportation, pollution emission in road transportation, especially the exhaust gas emission from the private vehicles and road freight transport vehicles shall be the most crucial constraints for Beijing transportation industry to reach the energy saving and emission reduction target responsibility and obtain the air pollution governance effect in 13th Five Year Plan (2016-2020). Combining the 12th Five Year Plan period (2010-2015) analysis of transportation industry and study on energy demand prediction of private vehicles in Beijing, and after using the predict technique of a exponential growth model and the moving average method to revise the predicted result, the study predicts a quantitative range of energy consumption demand in Beijing transportation industry for the next five years, meanwhile suggested a three-level estimation of the total control of the energy consumption target in Beijing transportation industry, and proposed policy suggestions for energy saving and consumption reduction in Beijing transportation area.

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.

How this classification was reachedexpand

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.862
Threshold uncertainty score0.411

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 communication0.0000.000
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 head0.009
GPT teacher head0.191
Teacher spread0.182 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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