Large-scale sampling of the freshwater microbiome suggests pollution-driven ecosystem changes
Bibliographic record
Abstract
Freshwater microbes play a crucial role in the global carbon cycle. Anthropogenic stressors that lead to changes in these microbial communities are likely to have profound consequences for freshwater ecosystems. Using field data from the coordinated sampling of 617 lakes, ponds, rivers, and streams by citizen scientists, we observed linkages between microbial community composition, light and chemical pollution, and greenhouse gas concentration. All sampled water bodies were net emitters of CO2, with higher concentrations in running waters, and increasing concentrations at higher latitudes. Light pollution occurred at 75% of sites, was higher in urban areas and along rivers, and had a measurable effect on the microbial alpha diversity. Genetic elements suggestive of chemical stress and antimicrobial resistances (IntI1, blaOX58) were found in 85% of sites, and were also more prevalent in urban streams and rivers. Light pollution and CO2 were significantly related to microbial community composition, with CO2 inversely related to microbial phototrophy. Results of synchronous nationwide sampling indicate that pollution-driven alterations to the freshwater microbiome lead to changes in CO2 production in natural waters and highlight the vulnerability of running waters to anthropogenic stressors.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".