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Record W2325463371 · doi:10.1386/tmsd.9.2.113_1

Canadian oil sands: How innovation and advanced technologies can support sustainable development

2010· article· en· W2325463371 on OpenAlexaffabout
Adam Bloomer, Kalinga Jagoda, Jeffrie Landry

Bibliographic record

VenueInternational Journal of Technology Management and Sustainable Development · 2010
Typearticle
Languageen
FieldEnergy
TopicGlobal Energy and Sustainability Research
Canadian institutionsMount Royal University
Fundersnot available
KeywordsCommercializationOil sandsSoftware deploymentBusinessSustainable developmentProcess (computing)Greenhouse gasEmerging technologiesPetroleum industryFossil fuelEnvironmental resource managementNatural resource economicsEnvironmental planningPolitical scienceEnvironmental scienceMarketingEngineeringEconomicsComputer scienceWaste managementGeology

Abstract

fetched live from OpenAlex

An increase in greenhouse gas emissions, coupled with elevated environmental awareness, has triggered the necessity of focusing on effective management of the Canadian oil sands. From Kyoto to Copenhagen, Canada's industry practices have been criticized. The deployment of sustainable production technologies is crucial for the survival of Canada's oil sands industry. Current literature is dominated by science and engineering scholars, yet a major problem appears to be a lack of full understanding of the multiple dimensions of the oil sands projects and the many players involved in innovation of sustainable technologies. A focused attempt to understand the development and commercialization process, with a view towards developing guidelines for individuals and institutions in managing innovation, would therefore be a useful contribution to the oil sands literature. This article proposes such an approach. The framework developed in this article is applied to a case study, with specific attention paid to identifying facilitating and inhibiting factors.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.882
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.005
GPT teacher head0.233
Teacher spread0.228 · 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

Citations9
Published2010
Admission routes2
Has abstractyes

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