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Record W2477757217 · doi:10.1139/er-2016-0033

Assessment of seven Canadian rivers in relation to stages in oil sands industrial development, 1972–2010

2016· article· en· W2477757217 on OpenAlexafffundvenueabout
Alexa C. Alexander, Patricia A. Chambers

Bibliographic record

VenueEnvironmental Reviews · 2016
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsEnvironment and Climate Change CanadaUniversity of New Brunswick
FundersNatural Sciences and Engineering Research Council of CanadaEnvironment and Climate Change CanadaU.S. Environmental Protection Agency
KeywordsEnvironmental scienceOil sandsTributaryHydrology (agriculture)Water qualitySurface runoffPhysical geographyAsphaltGeologyEcologyGeographyArchaeology

Abstract

fetched live from OpenAlex

We compiled a 38 year dataset (1972 to 2010) to evaluate changes in water chemistry over time in relation to oil sands mining activities. We examined patterns in three focal elements (dissolved selenium, dissolved arsenic, total vanadium) at 96 sites along seven tributaries of the Athabasca and Clearwater Rivers. Concentrations of the three focal elements did not differ between sites sampled upstream versus downstream of future development, between sites upstream versus downstream of the primary bitumen-bearing geologic formation, or at an upstream site over a 33 year period on the Muskeg River (1976–2009). Hence, longitudinal patterns observed in water chemistry following mine development cannot be attributed to natural background variation. In contrast, concentrations and loads of the three focal elements were greater post-development compared to reference values (p ≤ 0.01), and were typically greatest during the early exploration and land clearing stage of mine development (p < 0.01). Positive correlations between our three focal elements and 17 other variables either associated with bitumen or considered priority pollutants (r ≥ 0.13, p ≤ 0.04) suggest that the historical impacts of oil sands development on water quality extend beyond our three focal parameters. These results indicate that erosion and subsequent runoff associated with land clearing, construction, and early operational activities have affected water quality in the oil sands region.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.607
Threshold uncertainty score0.739

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.026
GPT teacher head0.260
Teacher spread0.235 · 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.

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

Citations50
Published2016
Admission routes4
Has abstractyes

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