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Record W1980983672 · doi:10.5539/mas.v2n4p131

Statistical Perspective and Pollution Indicator in Mengkabong Mangrove Sediment Sabah

2008· article· en· W1980983672 on OpenAlexvenueno aff
M. Radojević, Sarva Man­gala Praveena, Mohd Harun Abdullah

Bibliographic record

VenueModern Applied Science · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsnot available
FundersUniversiti Malaysia Sabah
KeywordsMangroveIntertidal zoneSedimentPollutionSeawaterEnvironmental scienceTidal flatOceanographyOrganic matterBayHydrology (agriculture)Environmental chemistryGeologyEcologyChemistryGeomorphologyBiology

Abstract

fetched live from OpenAlex

The role of tidal processes on intertidal surface sediments is frequently stated but rarely investigated due to the methodological restrictions. This study provides a basis for describing the tidal effects on the mangrove sediment. It reports investigations into the chemical composition of mangrove surface sediment in Mengkabong lagoon, Sabah, Malaysia. The levels of 16 parameters were determined in 33 surface sediment samples at high and low tides. The studied parameters showed high values at high tide compared to low tide. The tidal process showed there is a high contribution of seawater that brings additional ions such as Na, Mg and K into the mangrove system at high tide whereas at low tide, lower contribution of seawater was observed. Spearman correlation coefficient was used for the statistical characterizations of mangrove surface sediment at high and low tides. Relationships between physicochemical parameters, metals, granulometric fractions, organic matter and base cations at high and low tides were observed. Geoaccumulation Index calculations showed current sediment quality of Mengkabong mangrove sediments could be classified as having background concentrations of Al, Cu, Fe, and Zn, and as unpolluted with regard to Pb. These findings are important for future research which will explain the chemistry that lies in mangrove sediment.

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)0.001
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 score0.893
Threshold uncertainty score0.910

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
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 head0.011
GPT teacher head0.236
Teacher spread0.226 · 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

Citations6
Published2008
Admission routes1
Has abstractyes

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