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Record W7028559123

Evaluating the photo-enhanced toxicity of diluted bitumen and conventional heavy crude spills to freshwater organisms.

2023· dissertation· en· W7028559123 on OpenAlexafffundabout

Bibliographic record

VenueMspace (University of Manitoba) · 2023
Typedissertation
Languageen
FieldEnvironmental Science
TopicOil Spill Detection and Mitigation
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaMitacsGenome Canada
KeywordsHyalella aztecaEnvironmental remediationSpillagePetroleumWater pollutionOil spillWater qualityContamination
DOInot available

Abstract

fetched live from OpenAlex

The majority of Canada’s oil is extracted from the Alberta Oil Sands region, with the main products being diluted bitumen (dilbit) and conventional heavy crude (CHV). These products are transported across North America primarily by pipelines and rail. The effects of these petroleum products on freshwater environments after accidental spills are still poorly understood, even though the risks of oil spills into fresh water are increasing with more pipeline and rail transportation. Information regarding the efficacy of alternative remediation strategies for freshwater environments, particularly shorelines, are also lacking. This thesis addresses the effects of freshwater dilbit and CHV spills alone and in combination with ultraviolet (UV) radiation to evaluate the photo-enhanced toxicity (PET) of these oils. Model oil spills were conducted as a part of the IISD-Experimental Lakes Area (IISD-ELA) Freshwater Oil Spill Remediation Study (FOReSt) to evaluate minimally invasive remediation methods for shoreline environments after oil spills. Species with established sensitivities were chosen to evaluate the water accommodated fraction (WAF) of these oils before and after remediation, including Hyalella azteca and wild fathead minnows (FHM; Pimephales promelas). Juvenile Hyalella azteca were exposed to WAFs of weathered dilbit and CHV with various remediation treatments, including the surface washing agent (SWA) Corexit EC9580A and nutrient additions, referred to as enhanced Monitored Natural Recovery (EMNR). Dilbit and CHV both exhibited PET to Hyalella azteca by decreased growth and increasing mortality at lower lethal concentrations, measured as the concentration required to reach 50% mortality (low UV LC50s of 14,162 ng L-1 and 4761 ng L-1 compared to high UV LC50s of 9466 ng L-1 and 484 ng L-1 for dilbit and CHV respectively). Individuals exposed to Corexit EC9580A and its active surfactant dioctyl sodium sulfosuccinate (DOSS) incorporated bubbles internally and externally, disrupting buoyancy and increasing mortality. Early life-stages of wild FHMs were also exposed to WAFs of dilbit to evaluate its PET to a wild collected species at lower concentrations after remediation. Three exposure rounds were conducted at increasing post-oil spill intervals (12-, 20-, and 38-days post spill). FHM mortality was similar among all treatments, including the reference treatment, and lethal concentrations did not significantly differ between UV groups, however, the high UV group did have a lower LC25. In the last round, individuals in the high UV group had increased mortality compared to their low UV group counterpart, suggesting increased PET. Individuals in dilbit and dilbit with high UV treatments did exhibit significantly upregulated cyp1A expression and increases in malformations, particularly yolk sac edemas, cardiac malformations, and deflated swim bladders, indicating potential PET at sub-lethal levels. However, individuals exposed to the more weathered dilbit in later rounds showed phototoxic trends of increased mortality, upregulated cyp1A and thyroid related genes, and increased malformations, indicating dilbit photodegradation and weathering may potentially cause more PET. The results of these studies emphasize the importance of conducting toxicity testing under environmentally-relevant conditions, as real-world factors, such as UV radiation, can impact the toxicity to organisms in natural environments where accidental spills occur.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.694
Threshold uncertainty score0.644

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 head0.019
GPT teacher head0.246
Teacher spread0.227 · 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 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

Citations0
Published2023
Admission routes3
Has abstractyes

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