Catchment-scale assessment of groundwater discharge using ecological, thermal, and hydrochemical surveillance data in the Halton Region, Ontario
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
• Aggregation of >21,000 interpolated groundwater and stream monitoring data • Ecological parameters reflective of larger-scale groundwater discharge patterns • Temperature but not hydrochemical gradients reflect groundwater discharge dynamics • Surface water surveillance data can guide groundwater discharge monitoring efforts Groundwater-surface water exchange is spatiotemporally variable and costly to assess at high-resolution across large areas. This study explores the application of readily available surface water monitoring data to identify potential groundwater discharge locations in 15 catchments within Conservation Halton's jurisdiction within the Halton Region, Ontario, Canada. We first compared two interpolated groundwater discharge models and subsequently contrasted these against stream ecology data (macrophyte and fish taxa) and surface- and groundwater quality measurements that were aggregated to derive temperature and hydrochemical (alkalinity, chloride) gradients across the hyporheic zone. Both groundwater models agreed in their prediction of discharge locations for only 52% of monitoring sites, corroborating the need for further reconnaissance of potential discharge areas. Fish temperature preferences and ecological temperature classifications aligned reasonably with the groundwater discharge models (<55% of sites) and air-to-stream temperature differences agreed better with groundwater discharge predicted by modeling (p<0.04, R 2 >0.40) than stream-to-groundwater gradients (p<0.1, R 2 <0.25). Instead, hydrochemical signatures of both chloride and alkalinity in the streams were more ambiguous and displayed poor correlation with groundwater discharge maps and other monitoring parameters. Finally, we amalgamated the various investigated parameters into a classification scheme to determine the likelihood of groundwater discharge at the monitoring locations . This work exemplifies how combining commonly available monitoring information may be used to provide additional insight into groundwater discharge dynamics and stream health across larger and diverse catchment types where lack of in-situ monitoring or unverified numerical models complicate a clear understanding of groundwater discharge patterns.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 it