Virus Mineralization at Low pH in the Rio Tinto, Spain
Bibliographic record
Abstract
Water and sediment samples were collected from the Rio Tinto in southwestern Spain to assess (1) the presence and diversity of viruses in an acid mine drainage system and (2) determine if relationships occur between geochemical parameters and viral abundance. Epifluroescence microscopy and transmission electron microscopy revealed that viruses are not only present, but geochemical evidence and multivariate statistical analyses suggest that viruses in the Rio Tinto participate in mineralization processes. Viral capsids and tails occurred with iron-bearing minerals sorbed to their surfaces, at times with mineralization so extensive that differentiating between viral and inorganic particles using microscopy was difficult. Moreover, a strong inverse relationship between viral abundance and jarosite saturation state (Pearson correlation coefficient r = −0.71) was observed implying that viruses were removed from suspension owing to ongoing mineral precipitation (i.e., decreasing number of viruses with increasing rates of mineral precipitation, as inferred from saturation state). Viral-mineral interactions may additionally impact virus-host relationships as a weak correlation was found between viral and prokaryotic abundance, a relationship that is usually found to be highly correlated. Viral abundance and pH were strongly correlated (Pearson correlation coefficient r = 0.94) indicating viral sensitivity to low pH conditions.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".