Quantifying the interrelationship between tree stand growth rate and water table level in drained peatland sites within Central Finland
Bibliographic record
Abstract
The quantitative relationship between stand growth rate and water table level in peatland forest sites has not been fully ascertained in the literature. In this study, we investigated this relationship by means of a bivariate regression model. Tree and stand attributes, including volume and past 5-year volume growth as well as median water table depth (WTM) during the 1984 growing season, were observed in 69 Scots pine ( Pinus sylvestris L.) sample stands with three subplots established in each stand. All stands were located in deep-peated, moderately rich to poor organic soil sites in Central Finland (61°45′–62°26′N, 22°40′–28°29′E) that had been ditched for forestry about 25 years earlier (1959–1961). Prediction models for the fixed mean functions for 5-year volume growth and WTM as well as estimates for variances and the correlation of random effects at plot and subplot levels were estimated simultaneously using bivariate regression methods. The correlation of model residuals at the plot level was highly significant. The model was applied to simulate stand volume development for a period of 20 years. Simulations illustrated the dynamic interaction of stand volume, volume growth, and soil water levels: deep initial WTM resulted in stand growth and volume-development increases and subsequently further deepened the WTM in the stand. The model can be applied to southern boreal drained Scots pine peatlands to estimate the WTM in different stand volume conditions and to assess the effect of stand management on WTM.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".