The isotope hydrology of the Muskoka River Watershed, Ontario, Canada
Bibliographic record
Abstract
Abstract Stable isotope tracers of δ18O and δ2H are increasingly being applied in the study of water cycling in regional‐scale watersheds in which human activities, like river regulation, are important influences. In 2015, δ18O and δ2H were integrated into a water quality survey in the Muskoka River Watershed with the aim to provide new regional‐scale characterization of isotope hydrology in the 5,100‐km2 watershed located on the Canadian Shield in central Ontario, Canada. The forest dominated region includes ~78,000 ha of lakes, 42 water control structures, and 11 generating stations, categorized as “run of river.” Within the watershed, stable isotope tracers have long been integrated into hydrologic process studies of both headwater catchments and lakes. Here, monthly surveys of δ18O and δ2H in river flow were conducted in the watershed between April 2015 and November 2016 (173 surface water samples from 10 river stations). Temporal patterns of stable isotopes in river water reflect seasonal influences of snowmelt and summer‐time evaporative fractionation. Spatial patterns, including differences observed during extreme flood levels experienced in the spring of 2016, reflect variation in source contributions to river flow (e.g., snowmelt or groundwater versus evaporatively enriched lake storage), suggesting more local influences (e.g., glacial outwash deposits). Evidence of combined influences of source mixing and evaporative fractionation could, in future, support application of tracer‐enabled hydrological modelling, estimation of mean transit times and, as such, contribute to studies of water quality and water resources in the region.
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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 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".