Growth response of Norway spruce saplings in two forest gaps in the Swiss Alps to artificial browsing, infection with black snow mold, and competition by ground vegetation
Bibliographic record
Abstract
Black snow mold (Herpotrichia juniperi (Duby) Petr.) infection and browsing byungulates influence the growth of Norway spruce (Picea abies (L.) Karst.) saplings in subalpine forests in the European Alps. To isolate the impacts of artificial browsing (clipping of shoots) and snow mold infection on growth, we conducted a 2 year field experiment with planted saplings in two forest gaps in the subalpine zone of the Swiss Alps. In the first year (2003) saplings responded slightly positively to clipping and negatively to snow mold infection; sapling growth behavior was site-specific (ANOVA, r2 = 0.35). In 2004, saplings responded negatively to clipping, snow mold infection, long-lasting snow cover, and shading by ground vegetation (ANOVA, r2 = 0.59). The difference in mean annual growth rates between noninfected and infected saplings was large; long-lasting snow was found to enhance snow mold coverage. Removing these variables from general linear models strongly reduced model performance (d2 = 0.32 for the full model, d2 = 0.23 for no clipping, d2 = 0.16 for no snow cover). Sapling growth was negatively related to shading by ground vegetation, especially in 2004. We conclude that these biotic factors have a strong impact on growth, both individually and in combination, and that their effect is enhanced by interaction with environmental factors such as snow duration.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".