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Approach to Assessing the Effects of Aerial Deposition on Water Quality in the Alberta Oil Sands Region

2016· article· en· W2421413009 on OpenAlexafffundabout
Shadi Dayyani, Gillian L. Daly, Jerry Vandenberg

Bibliographic record

VenueWater Environment Research · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicToxic Organic Pollutants Impact
Canadian institutionsGolder Associates (Canada)
FundersUniversity of TorontoShell Canada
KeywordsSnowpackSnowmeltOil sandsEnvironmental scienceDeposition (geology)SnowHydrology (agriculture)SedimentEnvironmental chemistryContaminationWater qualityGeologyChemistryGeomorphologyEcology

Abstract

fetched live from OpenAlex

Snow cover forms a porous medium that acts as a receptor for aerially deposited polycyclic aromatic hydrocarbons (PAHs) and metals. The snowpack, acting as a temporary storage reservoir, releases contaminants accumulating over the winter during a relatively short melt period. This process could result in elevated concentrations of contaminants in melt water. Recent studies in the Alberta oil sands region have documented increases in snowpack and lake sediment concentrations; however, no studies have addressed the fate and transport of contaminants during the snowmelt period. This study describes modelling approaches that were developed to assess potential effects of aerially deposited PAHs and metals to snowpack and snowmelt water concentrations. The contribution of snowmelt to freshwater PAH concentrations is assessed using a dynamic, multi-compartmental fate model, and the contribution to metal concentrations is estimated using a mass-balance approach. The modelling approaches described herein were applied to two watersheds in the Alberta oil sands region for two planned oil sands developments. Accumulation of PAHs in a lake within the deposition zone was also modelled for comparison to observed concentrations.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.211
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.040
GPT teacher head0.322
Teacher spread0.282 · 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 designBench or experimental
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

Citations8
Published2016
Admission routes3
Has abstractyes

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