Long-term forest utilization can decrease forest floor microhabitat diversity: evidence from boreal Fennoscandia
Bibliographic record
Abstract
Forest floor microhabitat diversity was studied in old Pinus sylvestris L. dominated forest sites in two regions within the middle boreal vegetation zone in Fennoscandia: in 50 managed forest sites in the Häme region in southwestern Finland and in 45 natural or old selectively logged forest sites in the KuhmoViena region in northeastern Finland and northwestern Russia. The forests in the Häme region are characterized by a long history of forest utilization, while the forests in the KuhmoViena region can be regarded as natural or near natural. The managed forest sites in Häme had significantly lower forest floor microhabitat diversity compared with natural and near-natural forests. Microhabitats that were significantly more scarce in managed versus natural and near-natural forest sites included humps, depressions, decayed wood, and vicinity of decayed wood. On the other hand, even ground was significantly more abundant in managed forest compared with natural and near-natural forest. Microhabitat availability was also reflected in the occurrence of tree saplings growing in different microhabitats. The results suggest that long-term forest utilization has decreased forest floor microhabitat diversity. This has occurred because of a decreased amount of fallen deadwood and, possibly, lack of soil disturbances because of fewer uprootings caused by falling trees.
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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".