Influence of stand attributes and skid trail area on stand-scale ground flora diversity
Bibliographic record
Abstract
Mechanisation is increasingly used in European forest management, but so far, few studies have investigated the combined effects of stand attributes and skid trails on stand-scale ground flora diversity. Our study assessed the effects of stand attributes (age, stand type, basal area) and skid trail area on ground flora diversity in 400 m2 plots in oak forest stands in the northern half of France. We calculated the richness and abundance of ecological groups based on successional status and light preference. We developed and compared generalized linear models and assessed the magnitude of the effects of each variable. At the ecological group level, floristic variations among plots were mostly associated with stand attributes (stand type or basal area). Although we found non-negligible effects of skid trails on all herbaceous groups, these effects disappeared when tree stand attribute effects were incorporated into the statistical models. At the species level, when incorporating stand attribute effects into the models, skid trail area had weak or inconclusive effects on species (occurrence > 25%) abundance. Because mechanisation is a recent practice in European forests, stronger effects might be expected in the long term.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 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".