Stemflow infiltration areas into forest soils around American beech (<scp><i>Fagus grandifolia</i></scp> Ehrh.) trees
Bibliographic record
Abstract
Abstract The size of stemflow infiltration areas around the boles of trees is currently a topic of interest and debate within the hydrologic community. There is a gap in our knowledge of stemflow infiltration areas in many wooded ecosystems and a need for more than the few studies that have examined stemflow infiltration areas directly. Hence, this field study was specifically undertaken to mitigate the existing data gap by providing direct measurements of stemflow infiltration areas from high stemflow‐producing American beech (Fagus grandifolia Ehrh.) trees. Different stemflow rates (290, 72 and 31 L h−1) were simulated using dye‐infused stemflow and the areas of stemflow infiltration around four trees determined by measuring the areal extent of dye on the soil surface. Our results revealed that stemflow infiltration areas ranged from 0.0035 to 0.0951 m2 tree−1. The mean basal area funnelling ratio was 46.5 ± 1.8, whereas the funnelling ratios per unit infiltration areas, , were between 32.0 and 258.4. Despite intentionally high stemflow rates, chosen to compensate for the high infiltration capacities of forest soils, these results reinforce the fact that stemflow is an extremely localized input in natural forests. Thus, these results, even if specific to F. grandifolia within a particular forest and soil type, support a growing body of work indicating that stemflow infiltration areas are usually <1 m2, and often much smaller, in natural forests. Moreover, the high values of provide further evidence indicating that stemflow inputs are important for the development of hot spots in near‐trunk soils.
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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".