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Record W2271009580

여수 항만 지역의 퇴적물 중금속 함량 측정 연구

2015· article· ko· W2271009580 on OpenAlexaboutno aff
Sang‐Min Lee, Kyongsoo Han, 김동수, Seog-Ku Kim, Taeyoon Lee

Bibliographic record

Venue한국폐기물자원순환학회지 · 2015
Typearticle
Languageko
FieldAgricultural and Biological Sciences
TopicEcology and Conservation Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSedimentLoss on ignitionEnvironmental scienceEnrichment factorContaminationEnvironmental chemistryHeavy metalsGeologyChemistryGeomorphology
DOInot available

Abstract

fetched live from OpenAlex

This study was conducted to determine physico-chemical properties and degree of heavy metal contamination of sediments collected at Yeosu harbor. Outside of harbor (Y1, Y2, and Y3) showed relatively lower ignition loss than those for Y4 and Y5. All sediment samples had similar oxides and minerals based on the XRD and XRF analyses. Heavy metal contamination of the sediments was evaluated using USEPA, Ontario sediment quality guidelines, index of geoaccumulation and total enrichment factor. In summary, sediment Y5 was classified as moderately contaminated region based on index of geoaccumulation and heavily polluted based on standards of USEPA, Ontario sediment quality guidelines, and total enrichment factor. Sample Y5 obtained from the inner side of harbor showed the highest heavy metal contents. Among five heavy metals measured at Y5, Cu concentration was extremely high, which indicated that harbor activity might influence the Cu concentration in harbor 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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.999

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.003

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 head0.101
GPT teacher head0.260
Teacher spread0.160 · 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; both teacher heads agree on what is shown here.

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
Published2015
Admission routes1
Has abstractyes

Explore more

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