MétaCan
Menu
Back to cohort
Record W7020734135

Nature's Past Episode 050: Canadian Energy History

2015· other· en· W7020734135 on OpenAlexaboutno aff

Bibliographic record

VenueYork University Digital Library (York University) · 2015
Typeother
Languageen
FieldComputer Science
TopicAdversarial Robustness in Machine Learning
Canadian institutionsnot available
Fundersnot available
KeywordsEnergy consumptionPer capitaEnergy (signal processing)HistoriographyConsumption (sociology)Unit (ring theory)Natural historyPoliticsPolitical history
DOInot available

Abstract

fetched live from OpenAlex

According to a study by Richard Unger and John Thistle, Canadians consumed 430 petajoules of energy in 1867. Combining energy from animal labour, food, firewood, wind, water, coal, crude oil, natural gas and electricity, by 2004 Canadians reached a historic peak of energy consumption at 11,526 petajoules. For reference, a petajoule is a unit of energy measurement roughly equivalent to 31.6 million cubic metres of natural gas or 277.78 million kilowatt hours of electricity.
\n
\nSince Confederation, Canadians have been high per capita energy consumers and our appetites for energy have grown substantially over the past 148 years. The way we consume energy has changed quite a bit over that time period too. In 1867, Canadians drew energy primarily from organic sources: animal labour, wood, and agricultural produce. Since the mid-twentieth century, we have drawn increasingly from mineral sources of energy: coal, crude oil, and natural gas.
\n
\nThis shift in energy consumption since Confederation has arguably been one of the most consequential changes in Canadian history. It changed our relationships with one another as much as it changed our relationships with nature. The energy history of Canada is as much a concern for environmental history as it is for social history, political history, and cultural history.
\n
\nEnergy history is an emerging field in Canada, but one with long historiographical roots. To learn more about Canadian energy history and the development of this new approach to thinking about environment, history, and society, this episode features a round-table discussion with three Canadian historians each of whom were part of an energy history working group at the University of Toronto in 2014-15.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.111
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0000.003
Open science0.0050.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.150
Teacher spread0.143 · 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 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
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueYork University Digital Library (York University)Same topicAdversarial Robustness in Machine LearningFrench-language works237,207