AMS sustainable food truck : technology assessment & energy management
Bibliographic record
Abstract
The AMS at UBC has a goal to drastically reduce the Green House Gas \nemission in the next decade. For such manner, a sustainable food truck fits their \npurpose and the culture of the city of Vancouver. This project involves a simulation \nsystem as well as an energy management algorithm to predict the behaviour of the \nelectrical system of the truck and to best suit its needs, considering all the \nequipment is powered by solar panels (as a primary source), fuel cells (secondary \nsource) and a battery (back-up source). Also, the project includes the \nimplementation of each subsystem and their integration to the point where the \nrelevant data can be displayed on the screen. The user will be able to customize \nthe simulations, choosing which subsystem will be turned on or off. \nScenarios for the simulations were tested, such as a busy sunny day \n(during summer), providing enough data to state that the mobility system of the \ntruck may not be supported by the sources. Moreover, unless the technologies for \nequipment and energy generation are well chosen, energy shortage might be \nexpected in bad-case scenarios, such as a cloudy day. \nThe results of these simulations will be used to create and design \nspecifications of the truck's energy systems in future stages of the AMS \nSustainable Food Truck project. 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.”
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".