Measurement of water content as a control of particle entrainment by wind
Bibliographic record
Abstract
Abstract Of all controls on particle transport by wind, which include texture, crusting, vegetation cover and roughness, the role of water content is one of the most difficult to parameterize because of its high degree of spatial and temporal variability and its operation at a particle‐scale level directly at the surface. This study demonstrates that measurement of the distribution of brightness for all pixels in an image, now routinely employed in digital photography, is strongly correlated with gravimetric water content. Wind tunnel experiments further suggest that measurement of the distribution of β, as normalized against the brightness of the dry sand surface, is very useful in determining the order of magnitude of the mass transport rate (q). Finer resolution will likely never be achieved because of the heterogeneity of the particle transport phenomenon. Analysis of the variability in surface brightness does suggest that q is governed by the partitioning of momentum to particle motion that terminates in adhesion to surrounding areas of the surface that remain relatively wet. The proportion of surface particles that becomes dry appears to be of less importance. Preliminary work suggests that field application of digital photography in tracking spatial and temporal changes in the water content of beach deposits looks promising. Copyright © 2006 John Wiley & Sons, Ltd.
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 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) | 0.001 | 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".