Comparing biomass and nutrient removals of stems and fresh and predried whole trees in thinnings in two Norway spruce experiments
Bibliographic record
Abstract
In Denmark, thinning trees used for energy purposes are cut and left to dry in the stand before they are removed as whole trees. This practice causes shedding of needles and reduces nutrient removals for the benefit of long-term site fertility. It is uncertain, however, to what extent needles are shed and actual nutrient loss is affected by this practice. To address this question, we compared biomass and nutrient removals in two Norway spruce ( Picea abies (L.) Karst) experiments in western Denmark. Three contemporary thinning harvest intensities were examined: harvesting of fresh whole trees, predried whole trees, and stems only. The whole trees were chipped individually, and samples were removed to determine moisture and nutrient contents, whereas sample discs were removed from harvested stems. The biomass content of the cut whole trees was estimated to decrease 17% during predrying, whereas nutrient contents decreased 35%–60% for N, P, and K and <32% for Ca and Mg. The biomass content of stems was estimated to be 35%–42% lower than that of fresh whole trees. The corresponding differences in nutrient contents were in the range 84%–89% for N, P, and K and 73%–80% for Ca and Mg. Predrying and technological methods to reduce nutrient removals were compared and discussed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".