An integrated investigation of groundwater-surface-water interactions under conditions of changing climate in the Great Lakes Basin
Bibliographic record
Abstract
Fluctuations in temperature and precipitation associated with changing climate have the potential to influence hydrologic components, such as the timing and amount of groundwater recharge as well as temporal patterns in stream base flow. Variations in such factors may impact ecological functioning and human activities within a watershed, especially in water stressed, agriculturally dominated regions of the Great Lakes Basin. The potential impacts of climate change and subsequent feedback mechanisms are best examined through an integrated monitoring and modelling approach. In this manner, an improved understanding of a watershed's hydro-climatic functioning, especially groundwater-surface water interactions, may be obtained through multi-faceted, field based data collection and further supported by integrated numerical modelling. This research presents the Upper Parkhill Watershed in southwestern, Ontario (jurisdiction of the Ausable Bayfield Conservation Authority) as the location of an ongoing integrated investigation. This watershed features an Integrated Water and Climate Monitoring Station that has been collecting continuous data since 2012. Measurements from this station have been supplemented by site characterization as well as a water sampling program examining groundwater tracers, such as 222-Radon and electrical conductivity, in addition to stable isotopes (18O and 2H) and tritium to respectively assess locations of groundwater discharge, water origin, and age. This research supports the creation of an improved hydrogeologic conceptual model from which groundwater-surface water dynamics will be evaluated in the context of historical and potential future climate variability. It is anticipated that this study will provide a framework for the use of data from integrated monitoring stations and will also help to identify data gaps and variables of greatest importance for the purposes of integrated modelling.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".