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Record W3167149160 · doi:10.11575/prism/35871

2011-2012 University Of Calgary Scope 3 Ghg Inventory

2013· article· en· W3167149160 on OpenAlexaboutno aff
David Lee

Bibliographic record

VenuePRISM (University of Calgary) · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicSustainability in Higher Education
Canadian institutionsnot available
Fundersnot available
KeywordsScope (computer science)Greenhouse gasBusinessComputer science

Abstract

fetched live from OpenAlex

This research project conducts a Scope 3 GHG Inventory for the University of Calgary for the operating year 2011-12 using the World Resources Institute (WRI) Greenhouse Gas Protocol Corporate Value Chain (Scope 3) Accounting and Reporting Standard in order to identify the Scope 3 GHG footprint from university operations. Emissions from Scope 3 sources are important because they can make up to 75 percent of the total GHG footprint for an organization. Scope 3 is a relatively emerging category of emissions, and currently there is only a small amount of literature and guidance available. The previous inventory conducted in 2008-09 relied heavily on the Clean-Air Cool-Planet carbon calculator and generic calculation tools; however, this year’s inventory attempted to use emissions factors and data that provided a more accurate representation of the operating environment for the University of Calgary. This inventory shows a 45 percent increase from the last inventory with the largest source of emissions coming from student and staff commuting. In addition to calculating Scope 3 emissions for the University of Calgary, the report identifies some of the challenges in applying this type of reporting standard to a higher educational institution and recommends improvements required to continue to advance the quality and accuracy of Scope 3 reporting for the university in the future. This report finds that improvements in data quality and collection, development of improved reporting standards as well as emission factors and methodology that represent the local operating environment of the university will improve reporting in the future.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.356
Threshold uncertainty score0.994

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 studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.013
GPT teacher head0.231
Teacher spread0.219 · 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.

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
Published2013
Admission routes1
Has abstractyes

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