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Record W7007719856

ACW Baseline Report: Energy

2022· report· en· W7007719856 on OpenAlexaboutno aff

Bibliographic record

VenueYork University Digital Library (York University) · 2022
Typereport
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsFutures contractElectricityWork (physics)Electricity generationFossil fuelMains electricityBaseline (sea)Energy (signal processing)Energy policyNuclear power
DOInot available

Abstract

fetched live from OpenAlex

Presented at the Adapting Canadian Work and Workplaces (ACW) International Workshop in Toronto, Canada, November 2015. A review of all energy-related emissions is provided in this report, along with projections of future energy use. It is shown that oil and gas, transport, and buildings are the sectors most responsible for our increased emission profile. Growth in industrial and transport energy use will demand significantly more fossil fuel unless policy interventions push us towards ‘greener’ scenarios; using projections from the Trottier Energy Futures Project (TEFP 2016), two such scenarios are explored, one focused on sustainable urban development, and the other on a future where new electricity generation from nuclear sources is constrained. In both of these scenarios, the amount of electricity used in every sector increases dramatically. This suggests that a critical issue of the future will be designing new electricity generation in order to benefit both society and the workers who are engaged in the projects

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)0.002
metaresearch head score (Gemma)0.003
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: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.947
Threshold uncertainty score0.250

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.010
Science and technology studies0.0030.000
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0610.021

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 head0.021
GPT teacher head0.182
Teacher spread0.162 · 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
GenreOther

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
Published2022
Admission routes1
Has abstractyes

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