The resilience of the forest field layer to anthropogenic disturbances depends on site productivity
Bibliographic record
Abstract
The resilience of the boreal forest field layer (herbs plus dwarf shrubs) to anthropogenic disturbances is insufficiently understood because of the multitude of direct or indirect driver pathways and environmental conditions involved. We hypothesized that the impact of the forest-management-induced disturbances on field layer varies along the gradient of site productivity. To explore that we proposed a method for estimating the proportional effect of each driver on the field-layer composition, in a survey data of 273 mature or overgrown boreal forests, by combining variogram analysis with multifactorial general linear modelling. In forest types of very low and high productivity, field-layer composition was sensitive to the management disturbances in general and, particularly, to the management-controlled variations in the structure of the stand and its understory, i.e., in environmentally stressful conditions the main limiting factors were light availability and its spatiotemporal variability. At intermediate productivity, instead, the natural heterogeneity of ground layer conditions was the dominant driver, pointing to the limitation of regeneration microsites. Accordingly, on soils with low and high productivity, biodiversity-oriented sustainable forestry should diversify silvicultural approaches among stands and (or) enhance the within-stand mosaic, whereas small-scale natural disturbances of the ground-level “organic blanket” should be promoted in forests of intermediate productivity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".