Effects of liming and fertilization (N, PK) on stem growth, crown transparency, and needle element concentrations of <i>Picea abies</i> stands in southwestern Sweden
Bibliographic record
Abstract
Liming and (or) application of specific nutrients have been proposed as countermeasures to the acidification of forest soils in southern Sweden. The aim of this study was to investigate whether stem growth, crown transparency, and needle element concentrations of Picea abies (L.) Karst. growing on acidic mineral soils in southwestern Sweden are affected by additions of lime, lime plus P and K, or N in low doses (treatments: CaPK, Ca, N, CaPKN, and 2Ca2P2K). During the 5-year observation period following treatment with CaPKN there was a tendency to increased stem growth (possibly overestimated). The most plausible cause of this growth response was increased N availability in the soil, although the possibility that there was a minor effect of the PK application could not be excluded. None of the treatments affected the crown transparency. Several significant changes in elemental concentrations of current-year needles were associated with the treatments. The control plots in the experimental stands showed no severe damage or nutrient deficiencies despite the relatively acidic soils. These findings, together with the small treatment effects, suggest that there is no acute need for liming and (or) PK addition in P. abies stands on similar sites in southwestern Sweden.
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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".