Regeneration failure of Scots pine changes the species composition of young forests
Bibliographic record
Abstract
How seedling mortality and browsing affects species composition of regenerating forests has been mostly studied on a small scale. Yet, large-scale analyses based on extensive data are essential for robust prediction of species composition in young forests. In this study, we used a dataset from a national inventory of young forests (1–4 metres in height) to investigate the species composition of young forests across Sweden. We found that most of the regenerated forest area (almost 90%) was planted with Norway spruce (southern Sweden) and Scots pine (northern Sweden). Regeneration of Norway spruce was generally relatively successful but as a consequence of seedling mortality and browsing, almost 40% of the area regenerated with Scots pine will probably not develop into pine-dominated stands. Thus, low survival of Scots pine seedlings and trees can profoundly change the trajectory of species composition of the young forest from what was originally intended, and a large proportion of the young stands may develop into mixtures of conifers and broadleaves. While such mixtures may benefit certain biodiversity and ecosystem services, a loss of Scots pine dominated stands may also have adverse impacts on the economic returns as well as pine-dependent biodiversity and recreational values.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 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 teacher head, 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".