The role of tree stem proximity in the spatial variability of soil water repellency in a eucalypt plantation in coastal Portugal
Bibliographic record
Abstract
Certain organic compounds derived from living organisms or their decaying parts are generally accepted to induce soil water repellency. Water repellency may therefore be expected to increase with proximity to organisms releasing hydrophobic compounds. This hypothesis is tested here for Eucalyptus globulus trees, since eucalypt species are frequently associated with elevated repellency levels. In a young, first-rotation plantation on coastal dune sands in central Portugal, repeat measurements of water repellency using the ‘Molarity of an Ethanol Droplet’ (MED) test were carried out in situ between April 2001 and May 2002. On 25 dates, repellency was measured at initially 2 and later 3 distances on 2 sides of 8–11 randomly selected trees. On 15 occasions, additional repellency measurements were performed within small grids aside 3 of the selected trees. The postulated decrease in topsoil water repellency with increased distance from eucalypt tree stems was found to apply on several individual measurement dates, as well as, more unexpectedly since repellency usually is a transient phenomenon, for the study period as a whole. The results confirm the general association of eucalypt trees with water repellency, and indicate that tree stem proximity is an important but not sufficient factor to explain repellency distribution in topsoil.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".