Are nanomaterials leading to more efficient agriculture? Outputs from 2009 to 2022 research metadata analysis
Bibliographic record
Abstract
Agriculture is responsible for supplying food, feed, fibres, and an increasing fraction of fuel and raw chemicals for industry.Fulfilling such demands sustainably is one of the major challenges of our time.In this metadata analysis of 10 years of published research, we offer a quantitative overview of how scientists have been addressing the effects of nanoparticles on plants, and which were the main findings.If one hand, cultivated crops (55%) and plant nutrients (52%) are mostly employed as subjects of the studies, nanoparticle concentration ranges are much above, in general, more than twice that of those supplied by fertilizers or background soil concentration.Likewise, the time span of most studies, median 49 days for plants cultivated in soil, are still short compared to crop life-cycles (90-120 days), furthermore little attention (12% of treatments) has been dispensed to soil microorganisms.It was not possible to establish correlations between effects and experimental parameters such as concentration range, soil pH, or time of exposure, which reveals an intricate relationship between these results and experimental conditions.The small fraction of field experiments (5%) is also challenging to be transposed.Finally, the study raises the question of whether nanoscience will lead to incremental yield gains by replacing current inputs by nanotechnological ones, such as the controlled-release of fertilizers and pesticides, or it will disrupt agriculture by attacking problems so far not practically addressed such as hacking plant stress and defense mechanisms, and modulation of metabolism and photosystems.
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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.038 | 0.064 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.009 |
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".