Short-term development of a multilayered forest stand after target diameter harvest in southern Sweden
Bibliographic record
Abstract
Harvest strategies that emulate natural disturbances are being promoted to restore multilayered forest heterogeneity, notably the harvest of largest trees. However, their use also increases management complexity, and more information on their practical feasibility and effects on forests is needed. Therefore, in this study, target diameter cutting treatments were applied to a heterogeneously structured stand in southern Sweden (exemplifying a conifer-dominated forest of the hemiboreal forest region in northern Europe) to assess their effects on gap dynamics, natural regeneration, and stand growth. The target diameter cutting resulted in an exponentially decreasing gap size distribution, with the largest canopy gaps measuring 0.1–0.2 ha. The gap closure rate was higher than reported rates for gaps in natural forests. After 5 years, sufficient numbers of seedlings had regenerated, but proportions of intermediate- and late-successional tree species were low. The observed stand growth exceeded rates simulated using the growth model Standwise in the Heureka forest planning software package. The findings from this case study are useful for refining the general concept of close-to-nature forestry, but they need to be complemented with the information available from other regeneration studies after partial harvest.
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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".