Microbial Response to Fertilization in Contrasting Soil Materials used during Oil Sands Reclamation
Bibliographic record
Abstract
Reclamation practices following open‐pit mining typically include the reconstruction of soil‐like profiles using a combination of native soil materials, industrial by‐products, and fertilizers. Our overall objective was to compare the quality of eight soil materials used during reclamation in the Athabasca oil sands region of western Canada by characterizing their microbial communities as well as their response to a range of fertilization treatments. Materials included two carbon‐rich surface soil materials, four B horizons with varying extractable phosphorus and pH, the parent geological material (PGM), and tailings sands (TSS), a by‐product of oil extraction. Measured indices of microbial activity included the activities of b‐glucosidase, acid phosphatase, and phenol oxidase. Total biomass and structure of the soil microbial community were characterized based on phospholipid fatty acid (PLFA) analysis. Soil materials and fertilization treatments were tested with multivariate regression trees and non‐metric multidimensional scaling. Material type, rather than fertilization level, had the largest impact on all microbial parameters, including biomass, activity, and composition. Only the nutrient‐poor materials (PGM, TSS, and one of the B horizons) showed a response to fertilization. The microbial composition of three of the four B horizons was more similar to the two carbon‐rich surface soil materials than it was to PGM or TSS. Hence, we propose that these subsoil materials present an advantage over the use of the underlying PGM when reconstructing upland sandy soils. Finally, results indicated that soil microbial biomass could be used as a good indicator of seedling growth when no fertilizer was applied.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".