A model of wind-influenced leaf litterfall in a mixed hardwood forest
Bibliographic record
Abstract
Litterfall is an important ecological process in forest ecosystem functioning. Some attempts have been made to develop spatially explicit models of litterfall, but wind influence has never been included. Therefore, we studied the effect of wind on litterfall in an intimately mixed birch-oak forest using tree diameter and position as input data. After testing a litterfall model that assumed isotropic leaf dispersal, an anisotropic dispersal module was developed to account for wind influence. Using leaf fall data of 104 litter traps, isotropic and anisotropic models were optimized for silver birch (Betula pendula Roth), pedunculate oak (Quercus robur L.), and red oak (Quercus rubra L.) and model quality was compared. The anisotropic leaf litterfall model proved to be relevant because (i) the estimated litterfall directions corresponded very well to prevailing wind directions during leaf fall and (ii) including directionality significantly increased the goodness of fit of the models for both oak species but not for birch. Consequently, prevailing wind directions during leaf fall affected leaf dispersal in a broad-leaved deciduous forest. Insight into the spatial variability of the litter layer in forest ecosystems can benefit from the improved understanding of small-scale litterfall processes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".