Effects of different nutrient compensation treatments following forest fuel extraction on biomass of young Norway spruce (<i>Picea abies</i> (L.) Karst.)
Bibliographic record
Abstract
Whole tree harvesting of forests may require compensation for losses of nutrients and alkalinity. The effects of three different practical ameliorative methods on spruce allometry, total biomass, and nutrient uptake were studied in an experiment in a 3-year-old Norway spruce ( Picea abies (L.) Karst.) stand in south-west Sweden. The treatments were one application of the fine fraction of logging residues, one application of granulated wood ash, two applications of an N-free vitality fertiliser, and untreated control. Analysis of covariance showed that spruce needle and stem allometry depended on treatment. Spruce fine root allometry was very variable, showing no discernible effect of treatment. Fine root distribution was shallower in treatments with higher graminoid biomass (vitality and wood ash). Vitality treatment increased average concentrations of Ca, Mg, and Zn in spruce total biomass. Ash treatment only increased the Zn concentration. The average N concentration was similar between treatments. Spruce total biomass per unit area was inversely correlated with graminoid biomass. Measurements of N uptake in spruce and graminoid biomass indicated that there was competition for N between spruce and graminoids. Thus, the effect of nutrient compensation on competition needs to be considered when predicting the effect on the growth of the target species.
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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 | 0.001 |
| 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".