Benchmarking the Humidity-Dependent Mechanical Response of (Nano)fibrillated Cellulose and Dissolved Polysaccharides as Sustainable Sand Amendments
Bibliographic record
Abstract
Soil quality is one of the main limiting factor in the development of the food sector in arid areas, mainly due to its poor mechanics and lack of water retention. Soil's organic carbon is nearly absent in arid soils, though it is important for water and nutrient transport, to soil mechanics, to prevent erosion, and as a long-term carbon sink. In this study, we evaluate the potential benefits that are brought to inert sand by the incorporation of a range of, mainly, cellulosic networks in their polymeric or structured (fiber) forms, analogously to those found in healthy soils. We explore the impact of a wide range of nonfood polysaccharide-based amendments, including pulp fibers, nanocellulose, cellulose derivatives, and other readily available polysaccharide structures derived from arthropods (chitosan) or fruit peels (pectin) residues. A practical methodology is presented to form sand-polymer composites, which are evaluated for their soil mechanics as a function of humidity and the dynamics of their response to water. The mechanics are correlated to the network of polymers formed within the pores of the sandy soil, as observed by electron microscopy. The response to water is correlated to both the features of the network and the individual polysaccharides' physicochemical features. We expect this work to provide a rapid and reproducible methodology to benchmark sustainable organic amendments for arid soils.
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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 | 0.000 | 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".