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Record W7015318316

Spatial interpolation of improved groundwater recharge estimates on coarse textured soils

2016· dissertation· en· W7015318316 on OpenAlexaff

Bibliographic record

VenueMspace (University of Manitoba) · 2016
Typedissertation
Languageen
FieldEnvironmental Science
TopicGroundwater and Watershed Analysis
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsGroundwater rechargeHydrology (agriculture)KrigingVadose zoneDrainageGroundwaterGroundwater modelInterpolation (computer graphics)
DOInot available

Abstract

fetched live from OpenAlex

Groundwater recharge estimation is of fundamental significance to meet the agricultural water requirements, optimize water budget management, and further the sustainable development of water resources, particularly on coarse textured soil due to their rapid drainage behavior. The objectives of the study were to evaluate the feasibility and robustness of groundwater recharge estimation using one-dimensional physically based modelling coupled with weather stations and to interpolate the point estimates of recharge to a regional scale.Since the modelling results from different study areas coincided well with other studies, this method is feasible and robust to produce reliable point estimates of recharge universally. Four methods of recharge interpolation were applied in the second study area and were cross-validated by means of true percent error between the simulated and predicted recharge.The best prediction (7.8% true percent error) was obtained by ordinary kriging. Therefore, the methods of using physically based vadose zone modelling and kriging to estimate both points and regional recharge on coarse textured soil are feasible and extendable.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.589
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.008
GPT teacher head0.197
Teacher spread0.188 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2016
Admission routes1
Has abstractyes

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