Understory plant species-specific effects on subarctic soil fertility
Bibliographic record
Abstract
Although climate is the primary driver of soil fertility, plant functional traits can alter soil properties, creating a feedback between plants and soil fertility. In the lichen woodlands of the northern boreal forest, this feedback may be exemplified by slow growing ericaceous shrubs producing leaf litter that creates nutrient poor soils. We examined the soil of a lichen woodland in an area where fire had removed the organic layer 26 years previously and monospecific patches of ericaceous and non-ericaceous shrubs had developed, and the soil in an intact forest under the same species. In the burn site, soil inorganic N levels were four times higher under Salix candida Willd. than under Empetrum nigrum L. Soil from under S. candida in the forest also had a higher rate of respiration than soil under either ericaceous shrub. A growth assay with Leymus mollis, which is known to respond to nutrient additions in this region, showed twice as much growth in soil taken from under S. candida, regardless of whether the soil was collected from the forest or burn site. These results show that plant species can be a strong driver of soil fertility at a small spatial scale, even under harsh climatic conditions.
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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) | 0.002 | 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".