Amazonian soil fungi are efficient degraders of glyphosate herbicide; novel isolates of Penicillium, Aspergillus, and Trichoderma
Bibliographic record
Abstract
Pesticide residues that contaminate the environment circulate within the hydrological cycle can accumulate within the food chain and cause problems to both environmental and human health. Microbes, however, are well known for their metabolic versatility and the ability to degrade chemically stable substances, including recalcitrant xenobiotics. The current study focused on bio-prospecting within Amazonian rainforest soils to find novel strains fungi capable of efficiently degrading the agriculturally and environmentally ubiquitous herbicide, glyphosate. Of 50 fungal strains isolated (using culture media supplemented with glyphosate as the sole carbon-substrate), the majority were Penicillium strains (60%) and the others were Aspergillus and Trichoderma strains (26 and 8%, respectively). All 50 fungal isolates could use glyphosate as a phosphorous source. Eight of these isolates grew better on glyphosate-supplemented media than on regular Czapek Dox medium. LC-MS revealed that glyphosate degradation by Penicillium 4A21 resulted in sarcosine and aminomethylphosphonic acid.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 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".