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Record W7099296184

Financial support from Natural Resources Canada’s Renewable Energy Deployment Initiative is gratefully

2011· article· en· W7099296184 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicLaser-induced spectroscopy and plasma
Canadian institutionsnot available
Fundersnot available
KeywordsRenewable energyNatural resourceGovernment (linguistics)Feed-in tariffGreenhouse gasEnergy conservationEnergy policyEnvironmental impact of the energy industryRenewable resource
DOInot available

Abstract

fetched live from OpenAlex

acknowledged. It was provided to conduct the research on the current status and outlook of renewable energy technologies, post-secondary education programs and related training needs in the emerging renewable energy sources on which this report is based. This report was prepared by the ACCC’s Renewable Energy Advisory Committee on Training that includes representatives from the federal government, Canadian renewable energy industries and Canadian community colleges. ACCC appreciates the contributions of all partners engaged in the development of a national renewable energy training strategy. The views expressed in this report are not necessarily those of Natural Resources Canada, the Government of Canada or the Canadian renewable energy industries. Cover Photo: Kwantlen University College Langley Campus in Langley, BC Kwantlen University College has had ongoing interest in conservation for many years. A winner of three prestigious awards recognizing environmental stewardship, Kwantlen has reduced electrical energy consumption at its Langley and Richmond campuses by 36 and 45 percent respectively since 1995. Kwantlen has committed to reducing greenhouse gas emissions by 20 percent over the next three years. For more information on the energy and emission management plan of the University College please consult the web site at

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.858
Threshold uncertainty score0.998

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)0.0030.000

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 head0.179
Teacher spread0.165 · 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
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
Published2011
Admission routes1
Has abstractyes

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