On the Light Penetration in Natural Sands
Bibliographic record
Abstract
Sand-textured soils are found in a wide range of landscapes, from coastal areas to dune fields. The quantification of light penetration through these soils is of considerable interest not only for remote sensing applications, but also for agricultural, ecological and geophysical studies. Despite its importance, however, the literature on this topic is still scarce. Moreover, the available light penetration (transmittance) datasets for these soils are affected by experimental and modeling limitations. These include, for instance, the use of samples with morphological and mineralogical characteristics markedly different from those of naturally occurring sand-textured soils. To overcome these limitations and strengthen the current knowledge about light penetration in these soils, commonly referred to as natural sands, we propose the use of a predictive in silico experimental framework supported by measured data. Our findings demonstrate that it is necessary to properly account for the varying particle distributions and the iron oxide contents of natural sands in order to obtain penetration depth estimations that can be reliably employed in investigations involving these soils. In addition, our in silico experiments allow for a diversified assessment of the light transmission profiles of natural sands at different depths with respect to spectral and angular dependencies.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".