Monitoring mammals, birds and fish during summer 2022 at the outlet of an agricultural stream by mtDNA
Bibliographic record
Abstract
Abstract Fecal contamination of surface waters poses a potential risk to public and environmental health but can also impact the local economy and recreational activities. Determining its source could facilitate mitigation of the contamination. Fecal contamination can originate from several animals, particularly in areas where urban and agricultural activities overlap. In previous work, we developed a molecular approach to detect the presence of mammals, fish, and birds by sequencing mitochondrial DNA (mtDNA) amplicons derived from environmental DNA. In this report, we monitored the outlet of a stream located in an agricultural area for 16 weeks to detect the presence of mammals, including humans, livestock, domestic and wild mammals, birds and fish. We were able to detect mtDNA sequences affiliated to at least 73 animal lineages. Sequences affiliated to fish were proportionally the most abundant, followed by those affiliated to mammals. We observed increases in bovine and human mtDNA sequences after episodes of high flow in the watershed, suggesting that soil runoff to surface waters carried organic matter (e.g., manure, feces, wastewater) from these animals. Our approach could provide crucial information for farmers to mitigate fecal pollution generated by agricultural activities.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".