Landscape and Hydrologic Patterns' Impact on Microbial Activity: A Comparison of Arctic Watersheds
Bibliographic record
Abstract
Water security is a significant issue facing communities of the Canadian Arctic and is a fundamental component to the health, economic development, and ecological integrity of northern communities. Local water sources are under pressure from environmental changes and anthropogenic influences and need to be understood in order to protect them. The aim of this study was to collect landscape, hydrologic, and microbial baseline data in the communities of Baker Lake, Nunavut and Pond Inlet, Nunavut watersheds in order to, (1) characterize each of the study watersheds, (2) explore the relationships between precipitation, vegetation distributions, water chemistry, and microbiological indicator total coliforms (TC), (3) guide future research and source water management, (4) create capacity for northern communities to conduct watershed research. These research goals were guided by the residents of the study communities and volunteers collaborated to achieve this research in all part of the research process. Measurements were taken at two watersheds in Baker Lake, Nunavut during the summer of 2019 and six watersheds in Pond Inlet, Nunavut in the summers of 2017 and 2018. Watersheds that had higher proportions of Wet vegetation generally had higher TC measurements during the sampling season. In addition, the results showed that major precipitation correlated with higher TC measurements, though a 3-5 day lag between the precipitation event and the TC increase was found. The lag time between the spike in TC and the rain event varied temporally and spatially. The results suggest that watershed vegetation composition, slope, and hydrologic patterns influence the lag time between the “flushing” of the landscape and the response of microbiological indicators. The results will contribute to baseline knowledge which policy makers and the community can use to establish policies to ensure the health and sustainability of northern water sources. In addition, placing a emphasis on the exchange of knowledge with northern residents will facilitate the capacity for the communities to conduct their own research in the future. This local scale research is vital for northern communities to be able to prepare for the impacts climate change and development is having on their environment.
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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 | 0.001 | 0.002 |
| 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".