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Record W2264275680 · doi:10.14288/1.0108753

An investigation into a sustainable AMS food delivery system

2015· article· en· W2264275680 on OpenAlexaboutno aff
Cheng Kong, Xuan Jiang, Mohammad Meysamifard, Kevin Petersen

Bibliographic record

VenueOpen Collections · 2015
Typearticle
Languageen
FieldEngineering
TopicElectric Vehicles and Infrastructure
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessDelivery systemComputer scienceMedicineBiomedical engineering

Abstract

fetched live from OpenAlex

The new SUB at UBC will be completed December 2014 and is attempting to achieve LEED Platinum certification, a title given to the most sustainable buildings in the world. Not only should this certification refer to the materials used to construct the building and the construction process itself, but it should also influence the operations of the building and its businesses. The current delivery system used by AMS Food and Beverage to transport food throughout the campus is a gasoline powered vehicle. The primary stakeholder for this project, Collyn Chan, the sustainability coordinator of the new SUB, is concerned with the carbon emission of this vehicle and is requesting a recommendation for a more environmentally friendly vehicle to replace the current food delivery vehicle. This task is being investigated by a group of students currently enrolled in APSC 262, whom will determine an environmentally friendly vehicle option that can suitably serve as a food delivery vehicle for AMS Food and Beverage. This report describes a TBL analysis to evaluate which environmentally friendly vehicle would be the most suitable option as a delivery vehicle for AMS Food and Beverage. The three vehicle options compared in this report are a manual bicycle, a fully electric scooter and a fully electric car. Through the TBL analysis, the environmental, economic and social impacts associated with each vehicle’s capability to deliver food were compared. Additionally, the stakeholder also required that the vehicle recommendation be able to travel across campus in a timely manner, protect the deliverer and the food cargo from Vancouver’s weather throughout the entire calendar year and be able to carry at least 100 boxes of pizza in one trip. Only vehicles which were deemed to be relatively environmentally friendly and that met the constraints listed by the project stakeholder were considered in the TBL analysis; any other options were ruled out in a primary stage of the investigation. The result of the TBL analysis of the vehicle options considered determined that a fully electric car, such as the Smart Fortwo Electric Drive, or the Nissan Leaf 2014 SV, would be ideal for delivering food items throughout campus. The recommendation given by this report is that AMS Food and Beverage should replace their current gasoline powered food delivery vehicle with more environmentally friendly fully electric vehicles. Disclaimer: “UBC SEEDS provides students with the opportunity to share the findings of their studies, as well as their opinions, conclusions and recommendations with the UBC community. The reader should bear in mind that this is a student project/report and is not an official document of UBC. Furthermore readers should bear in mind that these reports may not reflect the current status of activities at UBC. We urge you to contact the research persons mentioned in a report or the SEEDS Coordinator about the current status of the subject matter of a project/report.”

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.010
GPT teacher head0.206
Teacher spread0.196 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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".

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Citations0
Published2015
Admission routes1
Has abstractyes

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