How Bacteria are Affected by Toxic Metal Release
Bibliographic record
Abstract
Metals and microbes are found everywhere on our planet; the ubiquity of each has led to their interactions over millennia, and has contributed to their reliance on each other to some degree. Some metals are essential to life, fulfilling chemical roles unattainable by organic molecules alone, while others are nonessential and have no known physiological role. All metals, however, are toxic in excessive concentrations, and can be found in the environment in excess due to anthropogenic activities. Over evolutionary history, microbes have accrued genetic adaptations to mediate the toxic effects of metal ion exposure; even so, no single strategy has been found to provide resistance to all toxic metals. This is likely a reflection of the physicochemical diversity of metal ions, as many metals ions can cause oxidative stress that can deplete cellular antioxidant reserves, which if overwhelmed can result in cellular damage or death. Other toxic mechanisms include replacement of metal co-factors in biomolecules, as well as mutagenic effects, microbial membrane damage, and/or affecting nutrient assimilation. Diversity is necessary for a healthy ecosystem, and these toxic effects of metal exposure can reduce microbial biodiversity in an environment.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".