Identification of taxa-specific responses to bioremediation treatments in hydrocarbon-contaminated Arctic soils
Bibliographic record
Abstract
A warming climate and improved technology have allowed northern countries to more thoroughly explore and exploit Arctic resources.This increased activity has led to an elevated risk of petroleum contamination, and consequently, there is a need to develop strategies to effectively and efficiently degrade these contaminants on site.While many Arctic soil microorganisms are known to naturally metabolize petroleum hydrocarbons in contaminated sites, a process known as bioremediation, treatments directed at stimulating the hydrocarbon-degrading activity of these microbes (e.g.nutrient amendments) have varied in effectiveness.The objective of this study was to determine whether microbial taxa respond equally to disturbances of the soil environment by hydrocarbon contaminants and nutrient amendments, and whether the most efficient hydrocarbon degraders are naturally stimulated.To determine whether the bacteria inhabiting contaminated Arctic soils assimilate added nitrogen equally, a novel 15N-stable isotope probing approach was developed.After a month of in situ incubation, it was determined that many hydrocarbon-degrading bacteria had incorporated the added nitrogen, but to varying extents.The Alphaproteobacteria most effectively used the added nitrogen, as determined by both 16S rRNA and alkB gene enrichment, and this was noteworthy given that they were not expected to be the most effective hydrocarbon-degrading group.To assess whether the relative abundance of bacterial taxa in hydrocarboncontaminated soils was determined by soil characteristics as opposed to hydrocarbondegrading ability, 18 soils from across the Arctic were collected and treated with diesel and monoammonium phosphate.Bacterial diversity and community composition were Thank you first to my supervisor, Dr. Charles Greer.The freedom that you gave me was frightening at first, but made me independent in ways that I hadn't been before.Thank you for supporting me, giving me the chance to fulfill a dream by traveling to the Arctic, and helping to shape even my strange ideas.Your positive and approachable nature is a huge bonus, and your door was always open when I needed input.Thanks also to my co-supervisor Dr. Lyle Whyte for valuable input, and for providing finances that allowed me to attend a conference outside of Montreal.Thanks to Dr. Étienne Yergeau for lessons in statistics, bioinformatics, and creating scientific papers.Thank you also for your help with networking, building my CV, and helping me to find a way to continue my career in Montreal.It was a great privilege to work with one of the rising stars in microbial ecology.Thank you to the staff and visitors of CFS-Alert,
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 source (direct Gemma or distilled Codex), 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".