Carbon and hydrogen isotopes of <i>n</i>‐alkanes in soils reconstructed after mining disturbance
Bibliographic record
Abstract
Abstract Ecosystem reconstruction after mining disturbance is a challenge considering the multitude of factors that affect soil formation and revegetation. In the boreal forest of western Canada, peat material is often used as the organic amendment for land reclamation to upland forest. Carbon and water dynamics of peat‐dominated ecosystems differ from natural upland forest soils. The objective of this work was to evaluate the evolution of soils reconstructed after mining disturbance using 13C and 2H analyses of n‐alkane tracers. Ten soils from natural ecosystems were sampled (0–10 cm) and compared with 11 soils from novel ecosystems ranging in age from 0 to 30 yr, as well as a fresh peat sample. Soils supported different vegetation, including pine (Pinus spp.), aspen (Populus spp.), and white spruce [Picea glauca (Moench) Voss]. Despite overlaps for some individual n‐alkanes, we found a dominance of n‐C25 in reconstructed soils, also dominant in the peat material, and a dominance of n‐C27 in natural soils, one of the dominant n‐alkanes in natural forest vegetation. In addition, there was a significant difference in odd n‐alkane δ2H and δ13C values between natural and reconstructed soils (p < .05). Differences in δ2H values, more negative for reconstructed soils than for natural soils, were attributed to changes in soil moisture, from wetter peat‐dominated soils to drier upland forests; among forest types, δ2H values were most negative under pine vegetation. The δ13C composition of odd n‐alkanes, in particular n‐C27, was significantly related to tree age (p < .05). Overall, both 2H and 13C isotopic signatures of odd n‐alkanes exhibited differences between natural and reconstructed soils. However, within the reconstructed soils, neither isotopic signature showed a clear evolution with age since reclamation.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".