Assessing structural and functional indicators of soil nitrogen availability in reclaimed forest ecosystems using <sup>15</sup>N-labelled aspen litter
Bibliographic record
Abstract
Landscape-level disturbance is a reality in many parts of the world including the Athabasca oil sands region, Canada, and soils play an essential part in the overall reclamation process. Soils are reconstructed during reclamation to provide a foundation and a nutrient source for the novel ecosystems. However, reclamation is often monitored through structural indicators of soil quality, which may not reflect dynamic ecosystem functions such as nutrient cycling. Our objective was to determine if nutrient cycling was occurring on novel ecosystems and if standard structural measures of soil quality were appropriate indicators. We assessed soil quality and nitrogen cycling in reclaimed, harvested and undisturbed aspen forest sites following the addition of 15N-labelled aspen (Populus tremuloides Michx.) leaf litter to the soil surface. Structural soil quality indicators, including soil moisture and microbial carbon and nitrogen biomass, were higher on the undisturbed site, whereas soil microbial composition differed among sites. Yet, uptake of 15N by microbes and plants, which continued throughout the 52 mo field incubation, was comparable across all sites. These results indicate that differences in structural attributes between disturbed and undisturbed soils do not necessarily translate into differences in soil functioning related to nitrogen cycling. Instead, this case study supports exploring the use of stable isotope tracers to assess dynamic soil function indicators in reclaimed ecosystems. Being able to follow biogeochemical cycling as vegetation becomes established and new forests start to develop following reclamation is key to assessing the long-term sustainability of these novel ecosystems.
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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".