Losses of nitrate from gaps of different sizes in a managed beech (<i>Fagus</i> <i>sylvatica</i>) forest
Bibliographic record
Abstract
In the ongoing discussion about sustainable forestry, gap regeneration is suggested to reduce nitrate (NO3) losses from forest ecosystems. The effect of gap formation and gap size on soil moisture and NO3 leaching was studied in two managed beech (Fagus sylvatica L.) stands in Denmark for about 2 years after formation of four gaps (approx. 20 and 30 m in diameter). Soil moisture content, soil solution NO3-N concentrations, and nitrogen (N) concentrations in throughfall and precipitation were measured along transects from the gaps into the surrounding forests. Losses of NO3-N were estimated using the water balance model WATBAL. Soil moisture content in gaps remained close to field capacity throughout the year, while it decreased to 50%70% of field capacity under the closed canopy during the growing season. Drainage water fluxes, soil solution NO3-N concentrations, and NO3-N losses were increased in the gaps as compared to under the canopy. For the whole study period, losses of NO3-N were 3- to 13-fold higher in the gaps than in the surrounding forests. However, a significant effect of gap size was not found within the range of the investigated gap diameters and canopy heights. Presumably, not only the aboveground canopy gaps, but also the belowground root gaps affected soil moisture and thus drainage water fluxes and NO3- losses.
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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) | 0.000 | 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".