ASSESSMENT OF PETROLEUM HYDROCARBON CONTAMINATION IN TERRESTRIAL ECOSYSTEMS USING CONVENTIONAL AND NOVEL APPROACHES
Bibliographic record
Abstract
Petroleum hydrocarbon (PHC) contamination has emerged as a pressing global concern, due to its impacts on human health and ecosystems, and presents a major challenge for remediation efforts. The heterogeneous nature of PHC mixtures and natural weathering processes that influence the bioavailability of individual compounds, make it difficult to predict the toxicity of PHC-contaminated sites. Despite their usefulness in accurately determining risks at PHC-contaminated sites, traditional toxicity tests are labourious and time-consuming. Hence, there is an urgent need to develop methods to predict PHC toxicity and modernize toxicity assessments to allow for high-throughput screening of impacted sites. The goals of this research were to assess the toxicity of weathered PHC-impacted soils to native organisms, improve the efficiency and accuracy of current toxicological tests, and identify molecular biomarkers in a model springtail species, Folsomia candida, suitable for high-throughput toxicity testing.\n\tWeathered PHC-contaminated soils from a Northwestern Canadian site were found to be non-toxic to native soil invertebrate and plant species despite exceeding federal PHC guidelines. Standard quantification methods of soil invertebrates following extraction from soils were deemed sub-optimal due to excessive invertebrate movement. Chill-coma induction and ethanol anesthesia methods were developed to temporarily immobilize soil invertebrates with no adverse affects, allowing for more precise quantification. Transcriptome analysis of F. candida exposed to PHCs linked reduced fecundity with diminished energy budgets caused by inhibition of carbohydrate metabolic processes and allocation of remaining energy to detoxify xenobiotics.\n\tThese findings contribute to the understanding of weathered PHC-contaminated sites and have the potential to significantly impact decisions that land-owners and risk assessors make regarding contaminated sites. Additionally, ecotoxicological values (e.g., 50% lethal concentration [LC50]) derived in this study differed by a factor of six when comparing weathered vs fresh crude oil contamination, highlighting the shortcomings of generic PHC guidelines and the necessity of employing high-throughput toxicity assays to derive site-specific guidelines. The findings in this thesis demonstrate the potential for molecular biomarkers in ecological risk assessments based on their ability to provide novel insights into PHC toxicity and modes-of-action, with a much shorter exposure duration than traditional growth endpoints.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".