Understanding Willow Transcriptional Response in the Context of Oil Sands Tailings Reclamation
Bibliographic record
Abstract
One of the reclamation objectives for treated oil sands tailings (OST) is to establish boreal forest communities that can integrate with the surrounding area. Hence, selection of appropriate soil reclamation cover designs and plant species for revegetation are important aspects of tailings landform reclamation and closure. Research and monitoring of the long term and immediate impacts of capped OST on the growth and survival of native boreal plant species are currently underway. However, plant responses to OST-associated toxicity are not well known at the molecular level. Using RNA sequencing, we examined the effects of three types of OST on the willow transcriptome under different capping strategies. The transcriptomic data showed that some genes respond universally and others in a specific manner to different types of OST. Among the dominant and shared upregulated genes, we found some encoding protein detoxification (PD), Cytochrome P450 (CYPs), glutathione S-transferase regulatory process (GST), UDP-glycosyltransferase (UGT), and ABC transporter and regulatory process associated proteins. Moreover, genes encoding several stress-responsive transcription factors (bZIP, BHLH, ERF, MYB, NAC, WRKY) were upregulated with OST-exposure, while high numbers of transcripts related to photosynthetic activity and chloroplast structure and function were downregulated. Overall, the expression of 40 genes was found consistent across all tailings types and capping strategies. The qPCR analysis of a subset of these shared genes suggested that they could reliably distinguish plants exposed to different OST associated stress. Our results indicated that it is possible to develop OST stress exposure biosensors merely based on changes in the level of expression of a relatively small set of genes. The outcomes of this study will further guide optimization of OST capping and revegetation technology by using knowledge based plant stress adaptation strategies.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".