Microbial response to carbon and nutrient additions in boreal forest soils and coversoils used during post-mining reclamation
Bibliographic record
Abstract
Two types of organic-matter-rich coversoils are used during reclamation in the oil sands region of Alberta: forest floor material (FFM) salvaged from upland forests, and peat material (PM) salvaged from boreal wetlands. In this study, we tested the hypothesis that carbon (C) and nutrient availability may limit microbial activity in these reclamation materials by measuring their response to either 13C-labeled glucose or NPKS addition. Coversoil materials were compared with two natural forest soils corresponding to target sites for reclamation. A shift in microbial community structure (determined using phospholipid fatty acid analysis) was detected after both additions, but it was stronger with glucose than NPKS, especially for the two reclamation materials. For all soils, the increase in microbial respiration was stronger after glucose than after NPKS addition. The majority of CO2 originated from soil organic matter (SOM) for the natural forest soils but from glucose for the reclamation materials. In PM, glucose addition triggered SOM mineralization, as shown by a positive priming effect. Despite the absence of a priming effect for FFM, microbial communities incorporated higher rates of glucose into their biomass and respired double the amount of glucose compared with the other materials. Furthermore, the overall microbial community structure in the FFM became more similar to that of the natural forest soil materials following glucose addition. These findings indicate that C and NPKS limitations were stronger for the two reclamation materials than for the two natural forest soils. Furthermore, microbial communities in the two reclamation materials responded more readily to labile C than to NPKS addition.
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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) | 0.001 | 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 | 0.000 |
| 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".