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.
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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) | 0.001 | 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) | 0.001 | 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".