Inter-disciplinary Characterization of Streambed Heterogeneity and its Influence on Groundwater-Stream Water Interactions
Bibliographic record
Abstract
It is well recognized that groundwater-surface water interactions influence the quantity and quality of various hydrogeological systems (rivers, lakes, streams). Groundwater-stream systems are an important investigative area for understanding fate and transport of nutrients and chemicals within the stream. While traditional methodologies are established to provide measurement and mapping of the spatial distribution of groundwater-stream interactions and exchange fluxes across a streambed, many can be invasive, labour intensive and suffer from low sampling density. The complexity in such systems is due largely to the heterogeneous nature both spatially and temporally. Given the strong control by streambed lithology on groundwater-surface water interactions, an improved measure of the spatial and temporal variations is desired. Geophysical techniques of DC-IP are an intriguing option as they can provide rapid, non-invasive and continuous information about the subsurface. The overall thesis objective was to evaluate the potential of 3D DC-IP for characterizing the structural heterogeneities within a streambed to inform assessment of groundwater-stream water interactions. High-resolution 3D DC-IP surveys were conducted in a 50m long headwater stream reach located in Kintore, Ontario. The resulting 3D distributions of resistivity and chargeability highlighted the heterogeneous nature of the streambed. Traditional characterization techniques were employed to evaluate the performance of DC-IP for mapping streambed composition and its associated influence on groundwater-stream exchanges. Strong concordance between DC-IP imaging and all the other traditional methods were determined, providing increased confidence in the ability of DC-IP to provide a valuable, non-invasive site tool to improve characterization of streambed heterogeneity and interpretation of groundwater-stream exchange patterns.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".