MétaCan
Menu
Back to cohort
Record W2120876963 · doi:10.2166/wqrjc.2012.027

Fish tainting in the Alberta oil sands region: a review of current knowledge

2012· review· en· W2120876963 on OpenAlexaffabout
Janelle L. Tolton, Rozlyn F. Young, Wendy V. Wismer, Phillip M. Fedorak

Bibliographic record

VenueWater Quality Research Journal · 2012
Typereview
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsOil sandsTailingsAsphaltEnvironmental scienceMining engineeringNatural (archaeology)PetroleumGeologyFish <Actinopterygii>Current (fluid)FisheryGeographyArchaeologyChemistryOceanographyPaleontology

Abstract

fetched live from OpenAlex

The Athabasca oil sands in northeastern Alberta, Canada represent the second largest petroleum reserve in the world. The process of extracting bitumen from the oil sands uses huge volumes of water, drawn from sources in the Athabasca River basin, and numerous mining companies operate adjacent to the river. Oil sands process-affected water (OSPW) from open pit mining is placed in large settling basins or tailings ponds that have the potential to leak. The goal is to eventually reclaim the tailings ponds to become functional ecosystems. Natural outcrops of oil sands in contact with surface waters also occur, and there are anecdotal reports in the media that fish caught near the Athabasca oil sands have an unusual flavor or odor. Several analytical and sensory studies have been undertaken to address this issue. Two major questions related to fish tainting arise: (1) Do the current oil sands mining, extraction and upgrading processes cause fish tainting in surrounding waters? (2) What is the tainting potential for fish that become established in reclaimed waters in the future? This review examines the types of compounds in OSPW that might contribute to tainting and the sensory science literature available related to fish tainting and the oil sands.

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)0.025
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.773
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0250.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.005
Insufficient payload (model declined to judge)0.0010.000

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 head0.388
GPT teacher head0.516
Teacher spread0.128 · 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
GenreReview

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

Citations7
Published2012
Admission routes2
Has abstractyes

Explore more

Same venueWater Quality Research JournalSame topicPetroleum Processing and AnalysisFrench-language works237,207