Soil water repellency under dry and wet antecedent weather conditions for selected land-cover types in the coastal zone of central Portugal
Bibliographic record
Abstract
This paper reports on the first systematic inventory of soil water repellency in Portuguese coastal dune sand areas. Since water repellency is widely associated with certain vegetation types or individual plant species, this inventory concerned arable land as well as 6 natural and semi-natural land-cover types representative for the vegetation zonation in the study area. Since water repellency further is a feature that commonly varies through time, disappearing when soils become wet, initial sampling was carried out during late summer 2000 and later repeated, at 1 of the 2 sites per land-cover type, during early spring 2001. Water repellency was principally measured in the field using the Molarity of an Ethanol Droplet (MED) test. Under the dry summer conditions, water repellency was a widespread phenomenon at and immediately below, the soil surface and numerous significant differences in ethanol classes existed between the land-cover types. The transient nature of water repellency was confirmed by many instances of significantly lower spring than summer ethanol classes. These significant differences were in general accompanied by a significant negative correlation of the summer and spring ethanol classes with volumetric soil moisture content. The sites’ overall repellency levels under dry antecedent weather conditions were significantly correlated with their overall levels of soil organic matter.
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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 | 0.001 | 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) | 0.001 | 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".