Intensive forest management — does it pay off financially on drained peatlands?
Bibliographic record
Abstract
There are only a few studies focusing on the financial aspects of management regimes on peatland forests and even fewer studies investigating intensive management. Such studies, however, are urgently needed, particularly in Finland, where a considerable proportion of drained peatlands is reaching a phase requiring active management. An empirical data set derived from the 10th National Forest Inventory (NFI10) is applied for stand-level simulations (MOTTI stand simulator) until final cut. The data are a representative sample of the most common drained peatland site types and their current stand structures in Finland. Based on several different initial stand conditions, tree growth was projected according to four management regimes: (1) passive management (only one clearcut executed), (2) management according to prevailing silvicultural recommendations, (3) stand-level optimum without ditch network maintenance (DNM) and fertilization (FERT), and (4) stand-level optimum with DNM and FERT (intensive management). The intensive management regime financially outperformed the other management options distinctively, regardless of the climatic region, peatland site type, and initial stand structure. However, towards more harsh climatic conditions and a more barren site type, the financial difference between management options flattened out, and silvicultural recommendations even resulted in a higher mean annual increment (MAI) compared with intensive management.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".