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Record W3041189593

하천 퇴적물 내 중금속 오염도 평가에 관한 연구 (낙동강 수계 표층 퇴적물을 대상으로)

2017· article· ko· W3041189593 on OpenAlexaboutno aff
김신, 정강영, 이권철, 권헌각, 신동석, 양득석

Bibliographic record

Venue한국물환경학회지 · 2017
Typearticle
Languageko
FieldAgricultural and Biological Sciences
TopicEcology and Conservation Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSedimentEnvironmental chemistryHeavy metalsContaminationPollutionEnvironmental scienceSiltGrain sizeTributaryMineralogyChemistryMetallurgyGeologyGeographyGeomorphologyMaterials science
DOInot available

Abstract

fetched live from OpenAlex

In order to certificate the contamination assessment of heavy metals in surface sediments from the Nakdong river. Surface sediments were collected of 24 sampling sites (main 14st., tributary 10st.) and analyzed for grain size heavy metals contents. Study area mainly composed of sand (avg. 94.1%) and mean grain size was 1.46 Φ on average. Heavy metals contents (avg. Al: 12.5%, Zn; 74.4, Cr: 45.3, Li: 26.0, Pb: 17.1, Ni: 10.5, Cu: 7.8, Cd: 0.22 mg/kg) were relatively high contents in the composed of fine sediments. In addition, the results of pearson`s correlation coefficient showed that most heavy metals and grain size (silt and clay) were highly correlated. The contents of Zn (6st.) and Ni (1st.) evaluated as moderately polluted, Zn (6st.) evaluated as LEL when compared with sediment quality standard of USEPA and Ontario sediment quality guidelines. Most heavy metals contents were I levels that dose not affected the benthos when compared with sediment pollution evaluation standard of NIER. The results of EX, EF, Igeo and CF showed the contents of Zn, Pb and Cd exceed the background contents and distributing of anthropogenic pollution and evaluated as moderately polluted level. And Nm-08 were relatively high level of contamination in the study area. However as results of PLI (less than 1), all sampling sites were considered unpolluted.

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 categoriesScience and technology studies, Insufficient 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 score0.069
Threshold uncertainty score0.998

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 studies0.0030.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.002

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 head0.067
GPT teacher head0.274
Teacher spread0.207 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

Explore more

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