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Record W7132970231

Study of the possible solutions to mitigate the environmental impacts generated by the exploitation of oil sands, in Athabasca, Canada

2022· dissertation· es· W7132970231 on OpenAlexaboutno aff
Paula Natalia Baquero Castro

Bibliographic record

VenueLumieres - Repositorio institucional Universidad de América · 2022
Typedissertation
Languagees
FieldEnergy
TopicGlobal Energy and Sustainability Research
Canadian institutionsnot available
Fundersnot available
KeywordsGreenhouse gasProduction (economics)Government (linguistics)Carbon capture and storage (timeline)Sustainable developmentProcess (computing)Order (exchange)Fossil fuelSustainability
DOInot available

Abstract

fetched live from OpenAlex

Canada has the majority of the world's unconventional oil sands reserves, located mainly in the province of Alberta. Based on the information gathered, it was possible to understand the production methods used by this industry and their respective environmental impacts. The emission of greenhouse gases during the exploitation and production process of these two techniques was specifically studied, once the levels of pollution emitted into the atmosphere were identified, we proceeded to look for some solutions proposed by different authors, as well as methodologies and technologies that the Canadian government has been applying in order to follow the sustainable development objectives that follow the framework of the 2030 agenda of the UN, such as the capture and storage of carbon dioxide, as well as its treatment and utilization, achieving the optimization of the use of natural resources.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.186

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.001
Scholarly communication0.0020.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.010
GPT teacher head0.247
Teacher spread0.237 · 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 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

Citations0
Published2022
Admission routes1
Has abstractyes

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