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HEAVY METAL CONCENTRATIONS IN SEDIMENT CORES FROM DIFFERENT MANGROVE FORESTS IN DA LOC COMMUNE, HAU LOC DISTRICT, THANH HOA PROVINCE, VIETNAM

2018· article· en· W2892738109 on OpenAlexaboutno aff
Minh Tran Thi

Bibliographic record

VenueVNU Journal of Science Earth and Environmental Sciences · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal wetland ecosystem dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsSiltMangroveSedimentSedimentary depositional environmentOrganic matterIntertidal zoneEnvironmental scienceSedimentary rockGeologyHydrology (agriculture)GeochemistryOceanographyEcologyGeomorphologyStructural basin

Abstract

fetched live from OpenAlex

In order to clarify heavy metal concentration profiles in different mangrove forest ages, four sediment cores (up to 100 cm in depth) were collected and tested for their physicochemical characteristics (pH, Eh, organic matters and grain size) and heavy metal concentrations. Results showed that mangrove sediments were composed mainly of sandy silt, silty sand, silt, sandy mud and mud. The average mud content in the sediment cores continuously increased with mangrove forest ages from one, nine, nineteen and twenty years old, whereas the mud content highly fluctuated with the core depth. pH, Eh and organic matter content implied that the sedimentary depositional environments were classified as weak alkaline and anaerobic. The Cu and Zn concentrations in the sediment cores from the intertidal zone exceeded the TEL values in Canadian Interim Marine Sediment Quality Guideline (ISQGs) values, while, Pb concentration exceeded the PEL level. When compared with National Technical Regulation on Sediment Quality QCVN43:2012/BTNMT, sediment samples were polluted by Cu and Pb.

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 categoriesScience 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 score0.025
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.006
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.008
GPT teacher head0.209
Teacher spread0.201 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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