Strong legacy effect of peat composition on physicochemical properties of reclamation coversoil
Bibliographic record
Abstract
Incorporation of salvaged peat in soil cover designs for oil sands mine reclamation is a common practice. However, current peat salvage practices do not differentiate between peatland types or the botanical composition of peat. In this study, we characterized the botanical composition of natural peat and coversoil on reclaimed sites and examined the influence of botanical composition on the physicochemical characteristics of reclaimed coversoil. Peat samples were collected from 15 natural peatlands (bog, poor fen, and rich fen) and peat coversoils were sampled from six reclaimed sites in the Athabasca oils sands region. The botanical compositions (Sphagnum, wood Sphagnum, woody, and woody/moss herbaceous) of all samples were determined. We found that natural peatland types had different physicochemical properties, primarily driven by Sphagnum-dominated samples with a high carbon:nitrogen (C:N) ratio and low total exchange capacity (TEC) when compared with samples dominated by more woody/moss herbaceous material. Similarly, we found that coversoil with Sphagnum-dominated peat compared with woody/moss herbaceous peat had lower TEC, pH, and total nitrogen values and higher C:N ratios (∼40 vs. 20 for Sphagnum and woody/moss herbaceous, respectively). Our results indicate that physicochemical properties driven by botanical composition remain in coversoil 5 yr after placement.
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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) | 0.001 | 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".