Bibliographic record
Abstract
Reducing greenhouse gas (GHG) emissions is increasingly recognized as a necessary step towards mitigating climate change (United Nations Framework Convention on Climate Change, 2016). The University of British Columbia (UBC) has pledged to reduce GHG emissions by 67% by 2020 compared to 2007 levels (UBC CAP 2010). Currently UBC is lacking a program to mitigate emissions from air travel. Air travel produces 2% of global emissions, but this number is expected to increase (Edwards et al., 2016). The goal of this project was to quantify air travel emissions from five UBC Vancouver Departments (Geography, Psychology, Theatre & Film, Chan Centre for the Performing Arts and the Institute for Resources, Environment, and Sustainability). A carbon calculator was created, and air travel information from an 18-month period was analyzed. In total, 709 trips were made with total emissions of 1070.25 tCO₂e. For reference, emissions from the Geography building are estimated to be 4.5-6 tCO₂e for the same period (Jamee DeSimone, personal communication, Nov 2016). Many of these trips were indirect (i.e. with layovers). Had the trips been direct, emissions would have been 981.93 tCO₂e. The primary trip purpose was to attend a conference (412), followed by travel done by non-UBC travellers (144), for example a guest lecturer at UBC. Most trips (609) were economy class. Average trip emissions were 1.51 tCO₂e, though this varies between departments. IRES reported the highest average trip emissions with 2.02 tCO₂e, while Psychology reported average trip emissions of 1.29 tCO₂e. It is recommended that economy class tickets be purchased for all UBC trips, and that direct flights be purchased whenever available. This will ensure that individual trip emissions are kept to a strict minimum. Furthermore it is recommended that trips be consolidated into fewer multi-purpose trips. Lastly the nature of non-UBC travellers work at UBC should be investigated, as they account for one fifth of total emissions. 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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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 source (direct Gemma or distilled Codex), 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".