Reclaimed soils, fertilizer, and bioavailable nutrients: Determining similarity with natural benchmarks over time
Bibliographic record
Abstract
Comparing functional similarity in reconstructed ecosystems with natural benchmarks can provide ecologically meaningful information to measure reclamation success. We examined nutrient supply rate using ion-exchange resins as a measure of ecosystem function in two oil sands reclaimed soils, viz. peat mineral mix (PMM) and forest floor mineral mix (FFMM), and measured fertilization effect on nutrient supply rates in these soils for three consecutive years contrasted with young-fire-disturbed and mature forest stands. Results indicated that nutrient profiles of reclaimed soils were significantly different than natural benchmarks. Phosphorus and potassium supply rates in reclaimed soils were up to 91% lower, whereas S, Ca, and Mg were, respectively, up to 95%, 62%, and 74% higher than in benchmark soils. The expected nutrient flush postfertilization was only apparent in N and P, but the transient effect levelled off the year after fertilization in most cases. Fertilization aligned the temporal trajectory of the nutrient profile in PMM similar to benchmark conditions indicating greater ecological benefit of fertilization than in FFMM. The findings from this study suggest that fertilization focusing on P and K is likely more ecologically appropriate for establishing natural ecosystem function on reclaimed soils in this region of the boreal forest.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| 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".