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Record W3164786371 · doi:10.3846/jeelm.2021.14834

GROUNDWATER QUALITY ASSESSMENT OF CHILANCHIL ABAY WATERSHED: THE CASE OF BAHIR-DAR CITY WASTE DISPOSAL SITE

2021· article· en· W3164786371 on OpenAlexaboutno aff
Dargie Haile, Nigus Gabbiye

Bibliographic record

VenueJournal of Environmental Engineering and Landscape Management · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGroundwater and Isotope Geochemistry
Canadian institutionsnot available
FundersBahir Dar University
KeywordsLeachateGroundwaterEnvironmental scienceWater qualityWatershedMunicipal solid wasteGroundwater pollutionPollutionEnvironmental engineeringHydrology (agriculture)Surface waterWatershed areaWater pollutionWaste managementAquiferEnvironmental chemistryGeologyEngineeringEcology

Abstract

fetched live from OpenAlex

Improper dump sites have served many years as an ultimate disposal site for all types of waste; municipal solid waste, industrial sewage and hazardous waste in developing countries such as Ethiopia. Physical, chemical and biological processes interact simultaneously to bring about the overall decomposition of the wastes (chemically laden leachate). If not, deal properly, such kind of dumping site can cause pollution to groundwater (because of Leachates) and surface water (through contaminant transportation by flooding and groundwater movements). Therefore, this study investigates the spatial and temporal variation of groundwater quality within the Chilanchil Abay watershed during dry and wet season due to the waste disposal site of the Bahir Dar city. Water samples were collected from 6 sampling points of groundwater from 30th March to 20th August by a monthly period. Over 10 water quality parameters such as pH, TDS, Electrical conductivity, Turbidity, Temperature, DO, BOD, COD, TC, NO3- and PO43–, were analyzed. Moreover, the overall status of the groundwater quality of the study area was evaluated by the Canadian Water Quality Index. Based on the result of this model the status of the groundwater sample points throughout the study area were raged from 42 to 46.2 (marginal status).

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score0.587

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 head0.006
GPT teacher head0.192
Teacher spread0.186 · 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

Citations5
Published2021
Admission routes1
Has abstractyes

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