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Record W4234855230 · doi:10.4095/306339

On the kriging of water table elevations using collateral information from a digital elevation model

2002· report· en· W4234855230 on OpenAlexaffabout
A. J. Desbarats, C Logan, M J Hinton, D R Sharpe

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsWater tableElevation (ballistics)Digital elevation modelKrigingGeologyAquiferHydrology (agriculture)VariogramPhreaticTable (database)GroundwaterSoil scienceGeomorphologyRemote sensingMathematicsGeotechnical engineeringStatisticsGeometry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.051
Threshold uncertainty score0.100

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.033
GPT teacher head0.232
Teacher spread0.199 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations15
Published2002
Admission routes2
Has abstractyes

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