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Record W3031356226

북한강 수계 호소퇴적물 오염도 평가

2019· article· ko· W3031356226 on OpenAlexaboutno aff
이보미, 성기선, 김국회, 문권영, 신명철, 홍정기, 김갑순, 유순주, 허인애, 노혜란

Bibliographic record

Venue한국물환경학회지 · 2019
Typearticle
Languageko
FieldAgricultural and Biological Sciences
TopicEcology and Conservation Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSedimentEnvironmental sciencePollutionNutrientHydrology (agriculture)Environmental chemistryEcologyGeologyBiologyChemistry
DOInot available

Abstract

fetched live from OpenAlex

The objective of this study was to investigate the pollution assessment of organic matters, nutrients, and metals in sediment in major lakes (5 lakes and 17 sites) of Buk-han river using various sediment pollution assessment guidelines and indices. The concentrations of organic matters and nutrients were lower than class IV (Sediment pollution assessment guidelines of Korea) and Severe Effect Level (SEL, Sediment pollution assessment guidelines of Ontario). This results indicated that the lakes sediments were unpolluted and tolerable level for sediment dwelling organisms by organic matters and nutrients. However, several sites of Chuncheon and Soyang lakes were evaluated “heavily polluted” level by organic index (Org-index). The order of lakes by metals concentrations from the one with the highest concentration was Hwacheon, Chuncheon, Cheongpyeong, Uiam, Soyang. All lakes except Hwacheon were assessed unpolluted to marginally and tolerable level for sediment dwelling organisms by metals based on sediment pollution assessment guidelines (Korean and Ontario), indices of geoaccumulation (Igeo), pollution loading (PLI) and ecological risk (RI). In Lake Hwacheon, every investigated sites were polluted with metals, especially Hg, based on sediment pollution assessment guideline of Korea and indices (Igeo, PLI and RI). The dwelling organisms in sediment of Hwacheon Lake were likely to be severed by metals.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.023
GPT teacher head0.217
Teacher spread0.194 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2019
Admission routes1
Has abstractyes

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