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Record W2557721036 · doi:10.3846/16486897.2016.1210155

GEOTECHNICAL CHARACTERISTICS OF EFFLUENT CONTAMINATED COHESIVE SOILS

2016· article· en· W2557721036 on OpenAlexaff
Muhammad Imran Khan, Muhammad Irfan, Mubashir Aziz, Ammad Hassan Khan

Bibliographic record

VenueJournal of Environmental Engineering and Landscape Management · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicLandfill Environmental Impact Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsLeachateEffluentAtterberg limitsSoil waterContaminationEnvironmental scienceConsolidation (business)Geotechnical engineeringEnvironmental engineeringWater contentWaste managementGeologySoil scienceEngineering

Abstract

fetched live from OpenAlex

In developing countries like Pakistan, raw industrial effluents are usually disposed-off directly into open lands or in water bodies resulting in soil contamination. Leachate formation due to rainfalls in openly dumped solid waste also adds to soil contamination. In this study, engineering behavior of soils contaminated by two industrial effluents, one from paper industry (acidic) and another from textile industry (basic), has been investigated. Laboratory testing revealed significant effects of effluent contamination on engineering behavior of tested soils. Liquid limit, plasticity index, optimum moisture content and compression index of tested soils were found to increase with effluent contaminant, indicating a deterioration in the engineering behavior of soils. Whereas maximum dry density, undrained shear strength and coefficient of consolidation of the contaminated soils showed a decreasing trend. The dilapidation in engineering characteristics of soils due to the addition of industrial effluents could pose serious threats to existing and future foundations in terms of loss of bearing capacity and increase in settlement. Keywords: soil contamination, industrial waste, engineering behavior, effluent waste, leachate.

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 score0.599
Threshold uncertainty score0.623

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 head0.003
GPT teacher head0.165
Teacher spread0.162 · 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

Citations24
Published2016
Admission routes1
Has abstractyes

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