Modelling volume growth response of young Scots pine (<i>Pinus sylvetris</i>) stands to N, P, and K fertilization in drained peatland sites in Finland
Bibliographic record
Abstract
Fertilizers are applied in drained peatland forests to correct nutrient imbalances, but few models have been developed for predicting the stand growth response. We used 10- to 34-year-long stem growth increment data from 51 fertilization experiments involving ash, PK, and NPK treatments (doses of P, K, and N varying between 35 and 45, 60 and 80, and 80 and 90 kg·ha–1, respectively) and controls to model the volume growth response of young Scots pine (Pinus sylvetris L.) stands to fertilization in drained peatland sites of middle and northern Finland. We used the linear mixed models method to express the stand-level growth response as a function of time (5-year periods), site type group, location, and the fertilization treatment (ash–PK combined and NPK). Different growth responses were obtained for nitrogen-rich sites with PK treatment and nitrogen-poor sites with NPK treatment. The PK fertilization in nitrogen-rich sites resulted in the largest volume growth response and longest temporal effect (1.0–2.0 m3·ha–1·a–1 higher mean growth for at least 35 years), with quicker and stronger effects in more southern conditions where average temperature sums were greater than 1000 degree-days (°C). NPK fertilization in nitrogen-poor sites gave a modest response (0.4–0.8 m3·ha–1·a–1 in 35 years).
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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 | 0.001 |
| 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".