Optimising stand management on peatlands: the case of northern Finland
Bibliographic record
Abstract
Peatland forests constitute a significant timber resource in several boreal countries. In Finland, peatlands have been intensively managed with large-scale drainage operations to enhance growth of the stands. In comparison with forests on mineral soils, a divergent feature in the management of peatland stands is the ditch network maintenance. We investigated the optimal forest management schedules for peatland stands using data from 17 Scots pine (Pinus sylvestris L.) sample stands representing the most important drained peatland site type in two climatic regions in northern Finland. We used the PIKAIA optimisation procedure (a genetic algorithm) to maximise the net present value of alternative management schedules involving ditch network maintenance operations and thinnings. Recent price and cost data were applied in the analyses. We found that the higher the interest rate, (i) the shorter the present generation’s rotation period and (ii) the less silvicultural activities were involved in the management schedule. Harsh climatic conditions emphasised this result, also showing that a decrease in the mean annual increment may lead to a significant increase in net present value. For each individual stand, the optimal stand management clearly outperformed (with respect to financial outcome) reference management that was mainly based on prevailing silvicultural recommendations. The results highlight the importance of interest rates in financial analyses of forestry: it is not self-evident that optimal stand management would include several silvicultural activities, such as ditch network maintenance and thinnings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".