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Record W4410788410 · doi:10.51264/inajl.v6i1.80

Water Quality Dynamics and Water Pollutions of Belawan Estuary, North Sumatra, Indonesia

2025· article· en· W4410788410 on OpenAlexaboutno aff
‪Ahmad Muhtadi, Rusdi Leidonald, Amanatul Fadhilah, Rizal Mukra, Decy M. Carolina Nasution

Bibliographic record

VenueIndonesian Journal of Limnology · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and Coastal Ecosystems
Canadian institutionsnot available
Fundersnot available
KeywordsEstuaryWater qualityEnvironmental scienceOceanographyGeographyGeologyEcologyBiology

Abstract

fetched live from OpenAlex

The Belawan Estuary is a highly strategic area in Medan, North Sumatra Province. Various utilisation activities such as ports, transportation, industry, fisheries, tourism, and settlements in the Belawan Estuary area have resulted in ecological pressures, particularly water pollution. The aim of this study was to determine the water quality dynamics and pollution status of the Belawan Estuary. The study was conducted in October 2023 in the Belawan Estuary, Medan City, North Sumatra Province. The sampling points consisted of eight locations representing the mouth, middle, and outer parts of the estuary. Sampling was conducted four times following the tidal cycle: full moon, last quarter, new moon, and first quarter. Water pollution status was determined using the pollution index (PI), Malaysian Marine Water Quality Index (MMWQI), and Canadian Council of Ministers of the Environment (CCME) methods. Temperature and pH were the most stable quality parameters. Total dissolved solids (TDS) and salinity fluctuated both spatially and temporally. Spatially and temporally, the Belawan Estuary falls into the moderately to heavily polluted category. The sources of pollution in the Belawan Estuary are urban activities, primarily the Terjun landfill. Nutrients and coliform bacteria are the main water quality parameters contributing to Belawan estuary pollution.

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 score0.008
Threshold uncertainty score0.367

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)0.0000.000

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 head0.007
GPT teacher head0.229
Teacher spread0.222 · 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
Published2025
Admission routes1
Has abstractyes

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