Nutrient cycling in two continuous cover scenarios for forest conversion of pine plantations on sandy soil. II. Nutrient cycling via throughfall deposition and seepage flux
Bibliographic record
Abstract
This study examined ion throughfall deposition and seepage fluxes in silver birch (Betula pendula L.) and Scots pine (Pinus sylvestris L.) regenerations a decade after a forest conversion intervention in two continuous cover scenarios (shelterwood cut versus group cut), as well as a 70-year-old control stand of Scots pine. Ion throughfall deposition was significantly influenced by the conversion scenario, being higher in the shelterwood cut than in the group cut. Compared with the control stand, nitrogen and acidifying throughfall deposition was significantly lower (–60%) in all regeneration types except for the birch regeneration under shelter. After shelter removal, ion throughfall deposition was significantly lower in all regeneration types than in the control stand. Seepage of NO3– and SO42– was significantly affected by the tree species, being higher in the birch than the pine regenerations, and SO42– seepage was significantly higher under shelter than in the group cut. After shelter removal, neither tree species nor scenario influenced the SO42– seepage, whereas the tree species still affected NO3– seepage. We conclude that the chosen forest conversion scenario is of profound influence on the ion throughfall and seepage fluxes during the first phase of a forest conversion process.
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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".