Moisture cycles of the forest floor organic layer (F and H layers) during drying
Bibliographic record
Abstract
The forest floor in many ecosystems consists of a partially decomposed organic layer (duff), which together with the litter layer comprises the boundary between the atmosphere and the mineral soil. Processes controlling the duff water budget during dry periods (which occur during most of the summer) were investigated using field monitoring, field flow exclusion manipulations, and coupled, multiphasic water and heat budget modeling. The objective of this paper is to model the significant processes that govern the dynamics of the duff water budget during drying. During dry periods the moisture content of the duff's F layer cycles diurnally with minimal moisture movement between the duff and mineral soil. Field exclusion of dew, lateral flow, and mineral soil flow suggests that diurnal drying cycles during the dry period are driven by diurnal atmospheric energy fluxes leading to coupled heat and mass fluxes within the duff. The fine root system and lateral flow do not typically influence drying. TOUGH2 was used to develop a one‐dimensional, multiphasic (both liquid and vapor) coupled water and heat budget model which confirmed that the vertical moisture fluxes lead to diurnal cycles. The model reproduced duff drying patterns with Nash‐Sutcliffe efficiencies and R 2 values greater than 0.910 and 0.970, respectively. Wavelet analysis indicates that the model and observed diurnal cycles in the upper layer's moisture contents are correlated at the 24 h scale. A model flux analysis reveals that lateral fluxes smaller than approximately 360 mm 3 h −1 would have little influence on the pattern of drying in the duff layer. Fluxes larger than approximately 5% of the total evaporative flux would slow duff drying and lead to behavior not observed in the field.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".