Agriculture 4.0: Polymer Hydrogels as Delivery Agents of Active Ingredients
Bibliographic record
Abstract
The evolution from conventional to modern agricultural practices, characterized by Agriculture 4.0 principles such as the application of innovative materials, smart water, and nutrition management, addresses the present-day challenges of food supply. In this context, polymer hydrogels have become a promising material for enhancing agricultural productivity due to their ability to retain and then release water, which can help alleviate the need for frequent irrigation in dryland environments. Furthermore, the controlled release of fertilizers by the hydrogels decreases chemical overdosing risks and the environmental impact associated with the use of agrochemicals. The potential of polymer hydrogels in sustainable agriculture and farming and their impact on soil quality is revealed by their ability to deliver nutritional and protective active ingredients. Thus, the impact of hydrogels on plant growth, development, and yield was discussed. The question of which hydrogels are more suitable for agriculture-natural or synthetic-is debatable, as both have their merits and drawbacks. An analysis of polymer hydrogel life cycles in terms of their initial material has shown the advantage of bio-based hydrogels, such as cellulose, lignin, starch, alginate, chitosan, and their derivatives and hybrids, aligning with sustainable practices and reducing dependence on non-renewable resources.
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 | 0.001 |
| 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".