Geospatial analysis reveals a hotspot of fecal bacteria in Canadian prairie lakes linked to agricultural non-point sources
Bibliographic record
Abstract
Lakes are sentinels of environmental changes within their watersheds including those induced by a changing climate and anthropogenic activities. In particular, contamination originating from point or non-point sources (NPS) within watersheds might be reflected in changes in the bacterial composition of lake water. We assessed the abundance of potentially pathogenic bacteria (PPB) sampled in 413 lakes within 8 southern Canadian ecozones that represent a wide diversity of lakes and watershed land use. The study objectives were (1) to explore the diversity of PPB; (2) to build a fecal multi-indicator from a cluster of co-occurring PPB; and (3) to predict the fecal multi-indicator over thousands of lakes. We identified bacterial taxa based on 16S rRNA amplicon sequencing and clustered 33 PPB matching taxa in the Canadian ePATHogen database using a Sørensen dissimilarity index on binary data across the sampled lakes. One cluster contained Erysipelothrix, Desulfovibrio, Bacteroides, Vibrio and Acholeplasma and was related to the NPS fraction of agriculture and pasture within the watershed as its main driver and thus it was determined as the fecal multi-indicator. We subsequently developed a fecal multi-indicator predictive model across 200 212 southern Canadian lakes which explained 55.1% of the deviance. Mapping the predictions showed higher fecal multi-indicator abundances in the Prairies and Boreal Plains compared to the other ecozones. These results represent the first attempt to map a potential fecal multi-indicator at the continental scale, which may be further improved in the future. Lastly, the study demonstrates the capacity of a multi-disciplinary approach leveraging both datasets derived from remote sensing and DNA sequencing to provide mapping information for public health governmental policies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.001 | 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".