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Record W4416027473 · doi:10.53894/ijirss.v8i11.10834

Evaluation of heavy metal contamination in sediments of the Linggi River, Malaysia

2025· article· W4416027473 on OpenAlexaboutno aff
Kumar Krishnan, Suganthi Suganthi, Prakash Balu

Bibliographic record

VenueInternational Journal of Innovative Research and Scientific Studies · 2025
Typearticle
Language
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsnot available
Fundersnot available
KeywordsContaminationArsenicPollutionChromiumSedimentManganeseWater pollutionZinc

Abstract

fetched live from OpenAlex

This study evaluates the concentrations and contamination levels of selected heavy metals (Mn, As, Cr, Fe, Zn, and Co) in surface sediments from seven locations along the Linggi River, Malaysia. Using Instrumental Neutron Activation Analysis (INAA), elemental concentrations were quantified, and contamination indices were applied to assess ecological risks. Results showed that arsenic (As) exhibited extremely high enrichment, with EF values ranging from 40.02 (S1) to 70.79 (S7), and Igeo values from 4.42 to 5.06, categorizing it as heavily to extremely polluted. Chromium (Cr) showed moderate enrichment (EF: 1.77–2.55; Igeo: –0.34 to 0.26), while zinc (Zn) had minor enrichment (EF: 1.01–1.51; Igeo: –0.92 to –0.57). Manganese (Mn), cobalt (Co), and iron (Fe) exhibited EF and Igeo values below 1 and 0, respectively, indicating minimal anthropogenic influence. The Pollution Load Index (PLI) values ranged from 1.02 (S5) to 1.37 (S7), confirming pollution across all sites, with S7 being the most contaminated. When compared against Canadian and Consensus Freshwater Sediment Quality Guidelines, arsenic and chromium levels exceeded Threshold Effect Levels (TEL) and Threshold Effect Concentrations (TEC), indicating significant ecological risk. These findings highlight the need for urgent environmental monitoring and pollution control in the Linggi River system.

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)0.033
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.267
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0330.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.006
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.153
GPT teacher head0.459
Teacher spread0.305 · 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.

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
Published2025
Admission routes1
Has abstractyes

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