Harnessing fungal biotechnology for sustainable management of arsenic contamination in agricultural ecosystems
Bibliographic record
Abstract
Arsenic (As) pollution of agricultural soil is of major concern due to its negative impacts on the human population worldwide, with exposure mainly occurring through food chain contamination. To alleviate As toxicity in humans, researchers have focused on restricting its entry into the food chain. Fungi are promising candidates for soil As detoxification as they possess different mechanisms to reduce As bioavailability in the soil through biosorption, bioaccumulation, biotransformation, and biomethylation/biovolatilization. The As-remediating potential of fungal strains can be improved by various biotechnological approaches, viz., protoplast-mediated transformation, restriction enzyme-mediated integration, CRISPR Cas9, etc. These techniques coupled with appropriate selection criteria and their validation through extensive in vitro and in situ (field) trials would result in genetically improved novel fungal strains that can be applied to As-contaminated agricultural fields for alleviating As toxicity in agro-ecosystems.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".