Evaluating the effects of distinct water saturation states on the light penetration depths of sand-textured soils
Bibliographic record
Abstract
The high-fidelity estimation of the light penetration depths of dry and wet sand-textured soils is of considerable interest for applied remote sensing and geoscience research initiatives involving a wide range of landscapes, from deserts and arable fields to coastal habitats. These initiatives include the restoration of vegetation in arid regions and the mitigation of weed dissemination in agricultural areas covered by wind-transported layers of these soils. Similarly, the remote detection and analysis of hyperspectral signatures from subsurface targets located in sandy landscapes also requires a sound understanding about the light penetration properties of the covering particulate materials under dry and wet conditions. Despite their relevance, however, there is a noticeable lack of data on the light penetration depths of sand-textured soils, notably accounting for their sensitivity to distinct patterns of water presence, either in their pore space or forming films around their grains. In this work, we aim to make inroads, both qualitatively and quantitatively, toward the understanding of key aspects associated with these interconnected processes. In order to achieve this goal without being constrained by laboratory and logistics limitations, we performed an array of controlled in silico experiments to systematically evaluate the effects of distinct water saturation states on the light penetration depths of representative samples of sand-textured soils. Our investigation was centered at the 400 to 1000 nm spectral domain, relevant for studies involving the mineralogy and morphology of natural sands, and it was carried out employing a first-principles simulation framework supported by actual measured data. By advancing the current knowledge in this area, our findings are expected to contribute to the development of new technologies aimed at the cost-effective monitoring and management of landscapes covered by natural sand deposits, and at the acquisition of more precise data on fundamental biophysical phenomena (e.g., seed germination) with a direct impact on crop yield and the recovery of ecosystems affected by the expansion of arid terrains.
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 | 0.000 |
| 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 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".