On the Red to Far-Red Ratios of Light Propagated by Sand-Textured Soils
Bibliographic record
Abstract
The expansion of landscapes formed by sand-textured soils is increasing due to aridity and desertification processes elicited by human activities and climate change. Vegetation restoration initiatives are instrumental to mitigate this trend. These initiatives involve the combined use of satellite, ground-based and in silico data in the detection and management of abiotic stress factors affecting seed germination and plant development in these regions. These photobiological phenomena, in turn, are often mediated by the red to far-red ratios of impinging light. In this paper, we examine the key differences between the red to far-red ratios of light propagated by sand-textured soils characterized by either a dominant presence of hematite or goethite (limonite), the mineral impurities largely responsible for their colors. Moreover, we also address the sensitivity of these ratios to different water distribution patterns: present in these soils' pore space or forming films around their constituent grains. By strengthening the current understanding about the interconnected effects of these abiotic factors, our findings are expected to contribute to the development of new cost-effective technologies for the monitoring (in situ and remotely) of sandy landscapes and the restoration of vegetation in these regions.
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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 | 0.000 |
| 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) | 0.002 | 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".