The occurrence of potentially pathogenic fungi and protists in Canadian lakes predicted using geomatics, in situ and satellite-derived variables: Towards a tele-epidemiological approach
Bibliographic record
Abstract
Eukaryotic pathogens including fungi and enteroparasites infect humans, animals and plants. As integrators of landscape catchment, lakes can reflect and record biological and geochemical events or anthropogenic changes and provide useful knowledge to formulate public health, food security and water policies to manage and prevent diseases. In this context, potentially pathogenic fungi and parasites were sampled using 18S rRNA gene amplicon sequencing in 382 lakes displaying a broad range of sizes and human impact on the watershed in 10 ecozones across Canada. Based on pathogen classifications from the ePATHogen database published by the Public Health Agency of Canada, we identified 23 health-relevant genera for human and animal hosts, including Cryptococcus and Cryptosporidium. Our study investigated the potential of remote sensing and geomatics to predict microbial contamination in a tele-epidemiological approach. We used boosted regression tree modeling to evaluate the probability of occurrence of the most common genera found in our dataset based on 10 satellite-derivable, geomatics and field survey variables which could be potential sources or transport mechanisms through the watershed or survival factors in the water. We found that southern ecozones that possess the highest agricultural and pasture activities tend to contain lakes with the largest number of potential pathogens including several fungi associated with plant diseases. Bio-optical factors, such as colored dissolved organic matter, were highly related to the occurrence of the genera, potentially by protecting against damage from ultraviolet light. Our results demonstrate the capability of tele-epidemiology to provide useful information to develop government policies for recreational and drinking water regulations as well as for food security.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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".