Physical properties of water in relation to stemflow leachate dynamics: implications for nutrient cycling
Bibliographic record
Abstract
Stemflow leachate chemistry from a deciduous canopy tree species monitored during late winter and early spring precipitation events demonstrated significant chemical enrichment. By considering stemflow volume and chemical concentration in relation to the quantity that would be expected in a rain gage occupying an area equivalent to the trunk basal area, manganese was found to be enriched by a mean factor of 1450 and potassium by a mean factor of 580. The most pronounced enrichment was documented during a late winter rain-on-snow event characterized by temperature oscillations near the freezing point. During this event, manganese was enriched by a factor of 4400 and potassium by 1715. We conclude that mixed precipitation events with multiple freeze-melt cycles can generate significantly more leachate than spring rainfall events because of lower air temperatures and increased kinematic viscosity and surface tension of stemflow drainage. These physical properties lengthen the residence time of intercepted precipitation on the woody frame of the tree and promote its funneling from inclined branches. Stemflow represents a spatially localized and enriched point input that may affect tree vigor in early spring. The influence of localized aqueous chemical fluxes to the forest floor on forest biogeochemistry and ecophysiological functioning are discussed.
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 | 0.001 | 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 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".