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Record W2561270275 · doi:10.1002/2016ef000484

Environmental consequences of oil production from oil sands

2016· article· en· W2561270275 on OpenAlexaboutno aff
Lorenzo Rosa, Kyle Frankel Davis, Maria Cristina Rulli, Paolo D’Odorico

Bibliographic record

VenueEarth s Future · 2016
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsnot available
FundersU.S. Geological Survey
KeywordsOil sandsEnvironmental scienceExtraction (chemistry)Oil productionUnconventional oilOil reservesWork (physics)Fossil fuelCrude oilEnvironmental protectionPetroleumPetroleum engineeringGeologyGeographyWaste managementAsphaltEngineeringArchaeology

Abstract

fetched live from OpenAlex

Abstract Crude oil from oil sands will constitute a substantial share of future global oil demand. Oil sands deposits account for a third of globally proven oil reserves, underlie large natural forested areas, and have extraction methods requiring large volumes of freshwater. Yet little work has been done to quantify some of the main environmental impacts of oil sands operations. Here we examine forest loss and water use for the world's major oil sands deposits. We calculate actual and potential rates of water use and forest loss both in Canadian deposits, where oil sands extraction is already taking place, and in other major deposits worldwide. We estimated that their exploitation, given projected production trends, could result in 1.31 km3 yr−1 of freshwater demand and 8700 km2 of forest loss. The expected escalation in oil sands extraction thus portends extensive environmental impacts.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.696
Threshold uncertainty score0.605

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.005
GPT teacher head0.191
Teacher spread0.186 · 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

Citations72
Published2016
Admission routes1
Has abstractyes

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