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Record W1860611479

[환경] 어류중 비소의 종분화 분석을 위한 초음파 추출법과 마이크로파 추출법의 비교

2003· article· ko· W1860611479 on OpenAlexaboutno aff
윤철호, 박용철, 홍종기

Bibliographic record

VenueAnalytical Science and Technology · 2003
Typearticle
Languageko
FieldAgricultural and Biological Sciences
TopicAgriculture, Soil, Plant Science
Canadian institutionsnot available
Fundersnot available
KeywordsArsenobetaineExtraction (chemistry)SonicationChemistryArsenateChromatographyNebulizerArsenicOrganic chemistryMedicine
DOInot available

Abstract

fetched live from OpenAlex

LC-ICP-MS를 이용한 어류중 비소의 종분화 분석을 위해 microwave-assisted extraction 과 sonication extraction 방법을 비교하였다. Ultrasonic nebulizer와 cross flow nebulizer를 사용한 비소종들의 검출한계는 유사한 결과를 보였다. 분석된 비소 종들은 arsenobetaine (AsB), arsenite [As(Ⅲ)], dimethylarsine acid (DMA), monomethylarsonic acid (MMA), arsenate [As(v)] 와 phenylarsonic acid (PAA) 이다. 두 가지 방법은 NRCC (National Research Council of Canada)의 표준물질인 DORM-2를 50% 메탄올로 추출하였다. arsenobetaine의 경우, 두 방법 모두 5% 이하의 상대표준편차와 82% 이상의 추출효율을 보였다. Arsenobetaine은 microwave assisted extraction 방법에서 14.18 ± 0.42 ㎎㎏-¹을 보였고 sonication extraction 방법에서는 13.54 ± 0.84 ㎎㎏-¹을 보였다. dimethylarsine acid (DMA)의 경우 각각 0.45 ± 0.06 ㎎㎏-¹과 0.44 ± 0.06 ㎎㎏-¹를 보였다.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.513
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.011
Science and technology studies0.0020.009
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.013
GPT teacher head0.227
Teacher spread0.214 · 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 designTheoretical or conceptual
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
Published2003
Admission routes1
Has abstractyes

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