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Record W1981156082 · doi:10.1109/smc.2014.6974373

Conflict analysis on water use and oil sands development in the Athabasca River

2014· article· en· W1981156082 on OpenAlexaffabout
Hanbin Kuang, D. Marc Kilgour, Keith W. Hipel

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldDecision Sciences
TopicGame Theory and Applications
Canadian institutionsWilfrid Laurier UniversityUniversity of Waterloo
Fundersnot available
KeywordsOil sandsAsphaltEnvironmental scienceUnconventional oilFossil fuelPetroleum engineeringEnvironmental engineeringWater resource managementGeologyWaste managementEngineeringGeography

Abstract

fetched live from OpenAlex

A conflict over water use and oil sands development in the Athabasca River Basin is modelled, and a stability analysis is carried out based on the Graph Model for Conflict Analysis. Oil sands require a great deal of water to extract bitumen, which may lead to insufficient water supply to the downstream area. Moreover, inappropriate waste water disposal can have serious effects on local fresh water resources. In order to balance industrial development and environmental protection, the conflict among the Alberta government, oil sands companies, and non-governmental organizations are analyzed, and insights and reasonable suggestions are provided.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.882
Threshold uncertainty score0.235

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.108
GPT teacher head0.346
Teacher spread0.238 · 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 designQualitative
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

Citations1
Published2014
Admission routes2
Has abstractyes

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