Root competition, not soil compaction, restricts access to soil resources for aspen on a reclaimed mine soil
Bibliographic record
Abstract
Restricted rooting space in response to soil compaction and belowground competition with herbaceous plants are two main limiting factors for successful reforestation after surface mining. Fine-textured, nutrient-rich soils with adequate soil moisture are particularly susceptible to both of these concerns and while there are recognized ways to manage competition, attempts to alleviate soil compaction through mechanical means have produced varying results. While roots of some herbaceous plants may penetrate compacted soil layers, possibly offering an alternative means to overcome physical restrictions, these potential benefits need to be weighed against negative effects from competition with planted trees. We examined the individual and combined impact of soil decompaction (deep tillage) and management of competing vegetation (herbicide) on soil properties, resource availability, and above- and below-ground growth of aspen (Populus tremuloides Michx.) seedlings on a reconstructed mine soil affected by severe subsoil compaction. Our findings suggest that although deep tillage reduced bulk density, this did not increase resource availability and had limited effect on seedling growth. In contrast, competition with smooth brome grass (Bromus inermis Leyss.) drastically reduced aspen belowground growth because the grass rapidly occupied available rooting space, while simultaneously lowering the availability of water and nutrients, in particular nitrogen.
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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) | 0.001 | 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".