On the kriging of water table elevations using collateral information from a digital elevation model
Bibliographic record
Abstract
In unconfined aquifers flowing under topographic gradients, the water table is a subdued replica of the ground surface above. This principle is the basis for using detailed collateral or secondary information from digital elevation models to supplement sparse observations from water wells in the mapping of phreatic surfaces. Data from DEM-derived secondary variables are incorporated into the estimation of water table elevations using the geostatistical method known as kriging with an external drift (KED). Two different KED models are proposed based on the choice of secondary variable. In the first, water table elevation is expressed as the sum of a deterministic trend given by topographic elevation and a residual random component representing depth to water table. In the second, depth to water table is expressed as a linear function of a deterministic trend, given by the TOPMODEL topographic index, and a residual random error. The relationship between water table depth and topographic index is derived from simplified groundwater dynamics and forms the basis of TOPMODEL-type rainfall-runoff models. The two KED models are applied to the mapping of water table elevations in the Oak Ridges Moraine (ORM), an unconfined aquifer near Toronto, Canada. Results show that KED with topographic elevation as external drift is the more robust of the two models. Despite its strong theoretical basis, the second model yields kriged water table elevations that are not always physically plausible. In part, this is because field observations of water table depth do not verify the predicated relationship with topographic index in large parts of the study area. However, this relationship may be valid in other cases and at other spatial scales. In such cases, the second model would provide a very powerful approach for mapping water table elevations and for calibrating distributed parameters of the TOPMODEL equations on water well observations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".