Seed regeneration aided by nanomaterials in a climate change scenario: A comprehensive review
Bibliographic record
Abstract
Abstract Nanotechnology has demonstrated its potential for advancing sustainable agriculture. This article explores new advancements in nanotechnology in agriculture, including plant extraction and validation, by emphasizing nano-fertilizers, nano-pesticides, nano-biosensors, and nanoenergy recycling processes. Nanomaterials are important for the formation, transport, and degradation of soil toxins and are a fundamental starting point for various biotic and abiotic rehabilitation processes. Research on nanoparticles’ remediation applications and soil stay insufficient and are generally restricted. When integrated into agricultural systems, nanomaterials may influence the soil quality and plant development examined by setting their impacts on supplement discharge in target soils, soil biota, soil natural matter, and plant morphological and physiological reactions. The current research works show that the seed coat acts as a barrier to nanomaterial penetration, in which both the seed coat and cell wall allowed easy water passage. Additionally, the uptake, movement, and associated defense mechanisms of nanomaterials within plants have been investigated. Future research directions have been identified to further the study toward the sustainable development of nano-enabled agriculture.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".