Tree influence on carbon stock and C:N ratio of soil organic layer in boreal Scots pine forests
Bibliographic record
Abstract
Observed small-scale spatial variation of forest soil is suggested to be produced by tree influence. We examined spatial variation of the tree influences by modelling tree influence potential that accounts for location and size of trees. Thereafter, we tested significance of the correlations between the tree influence potential and soil properties (carbon stock and C:N ratio of the organic layer) with Monte Carlo permutation tests. The methods were applied to five Scots pine stands located in the boreal vegetation zone in Finland. We found statistically significant tree influence on soil C:N ratio in all studied stands, but the tree influence on soil carbon stock was significant only in three of five stands. This indicates that location of trees has a relatively steady and remarkable influence on spatial variation of a soil parameter that reflects soil fertility and nutrient balance, but variation of soil carbon stock is not that clearly affected by current tree stand. The correlations between C:N ratios and tree influences were positive in four of the five stands, indicating that soil was less fertile near the trees. The methods described here produce statistically reliable information pertaining to the influence of trees on soil properties.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".