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Record W4386553853 · doi:10.11159/icepr23.141

A Modelling Framework for Groundwater Sustainability in the UpperOrange Catchment of South Africa

2023· article· en· W4386553853 on OpenAlexvenueno aff
Rebecca Alowo

Bibliographic record

VenueProceedings of the World Congress on New Technologies · 2023
Typearticle
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsnot available
Fundersnot available
KeywordsSustainabilityOrange (colour)GroundwaterWater resource managementEnvironmental scienceDrainage basinHydrology (agriculture)GeologyGeographyGeotechnical engineeringChemistryEcology

Abstract

fetched live from OpenAlex

Sustainability modelling of the C52 Upper Orange Catchment was done for 52 boreholes.This Modelling framework was developed because in arid and semi-arid areas of South Africa, farmers and communities only have a limited number of water provision points.This has put more pressure and increased the number of wells and boreholes being drilled where they can access groundwater which is needed for multiple purposes especially agriculture and drinking water provision.Excessive pumping can lead to groundwater depletion, where groundwater is extracted from an aquifer at a rate faster than it can be replenished.This will put undue pressure on aquifers and catchments such as the Upper Orange (Modder).The methodology involved a detailed understanding of the parameters and ranking of the physical processes affecting groundwater system of the upper orange river catchment for 51 boreholes such as the climatic factors, aquifer system, rights, and equity.This model assessed whether there is undue pressure on the Upper Orange Catchment.The result and findings have been presented in a sustainability index.The outcome was a sustainability map showing areas depicting the most to least sustainable aquifers in the catchment.The developed sustainability index and maps are useful tools for future groundwater management.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.404
Threshold uncertainty score0.313

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.025
GPT teacher head0.234
Teacher spread0.210 · 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 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

Citations1
Published2023
Admission routes1
Has abstractyes

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