Micronutrient concentrations vary between peat–mineral mix and substrates in revegetated sites in the Alberta oil sands
Bibliographic record
Abstract
Adequate supply of micronutrients is essential for plant growth in reclaimed sites in the Athabasca oil sands region. The objectives of this study were to determine boron, iron, manganese, copper, and zinc concentrations in peat–mineral mix (PMM), tailings sand (TS), and overburden (OB) materials and to assess whether lodgepole pine (Pinus contorta) planted on PMM over TS and white spruce (Picea glauca) planted on PMM over OB had low foliar micronutrient concentrations. Micronutrient concentrations determined using LiNO3 and Mehlich-3 extractions were different between PMM and TS in the pine sites while only LiNO3 extractable boron was different between PMM and OB in the spruce sites (p < 0.05). Micronutrient concentrations varied in the order of boron > iron > manganese > zinc > copper in all soil layers with concentrations ranging from 0.04 to 39.56 μg g−1. The low foliar concentration of copper in pine and spruce was consistent with low LiNO3 extractable copper in the soil in both the pine and spruce sites. We conclude that the availability of micronutrients such as copper can become a potential limitation for revegetation of white spruce but not lodgepole pine. Further studies on soil management for improving Cu availability in reclamation materials are needed for improving the growth of spruce in reclaimed soils.
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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 | 0.001 | 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) | 0.001 | 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".