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Record W2799305943 · doi:10.25336/csp29368

Energy, resource consumption, and climate change

2018· article· en· W2799305943 on OpenAlexaffvenueabout
Don Kerr

Bibliographic record

VenueCanadian Studies in Population · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsThe King's University
Fundersnot available
KeywordsGeographyClimate changePopulationGreenhouse gasPolitical scienceForestryDemographyEcologySociology

Abstract

fetched live from OpenAlex

Population growth, at both the national and global level, will most certainly impact Canada’s climate, and, more broadly, its environment. While Canada’s population has been projected to continue to grow for many decades, what happens elsewhere in terms of population growth will be particularly important to Canada. Although greenhouse gas emissions (GHGs) in Canada have levelled off somewhat over the last decade and a half, global emissions have continued to climb. As a direct result, with increased GHGs in the atmosphere, Canada’s northern climate has already been impacted in a major way with considerable warming, particularly in its most northern forests and Arctic ecosystems.L’accroissement de la population, autant à l’échelle nationale que mondiale, aura certainement un effet sur le climat au Canada et, plus largement, sur son environnement. Selon les projections, la population canadienne devrait continuer à augmenter pendant encore plusieurs décennies. Or, ce qui se passe ailleurs en termes d’accroissement de la population sera particulièrement important pour le Canada. Bien que les émissions de gaz à effet de serre (GES) au Canada se soient nivelées au cours de la dernière décennie et demie, les émissions globales ont cependant continué à grimper. En conséquence directe de cette augmentation de GES dans l’atmosphère, le climat dans le nord du Canada a déjà subi un impact majeur par un réchauffement important, surtout dans les forêts les plus au nord et les écosystèmes arctiques.Mots-clés : population et environnement; climat; utilisation d’énergie; pointe de population

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.418
Threshold uncertainty score0.928

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.033
GPT teacher head0.258
Teacher spread0.226 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations2
Published2018
Admission routes3
Has abstractyes

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