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Record W2908170460 · doi:10.14203/jkti.v16i2.9

PENENTUAN LOGAM BESI DAN SENG TOTAL DALAM PRODUK PERIKANAN MENGGUNAKAN FLAME ATOMIC ABSORPTION SPECTROMETRY DAN PENGUKURAN NILAI KETIDAKPASTIANNYA

2014· article· id· W2908170460 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJurnal Kimia Terapan Indonesia · 2014
Typearticle
Languageid
FieldEnvironmental Science
TopicHeavy Metal Pollution Remediation
Canadian institutionsnot available
Fundersnot available
KeywordsPhysicsCertified reference materialsAnalytical Chemistry (journal)Nuclear chemistryHeavy metalsAtomic absorption spectroscopyEnvironmental chemistryChemistryChromatographyDetection limit

Abstract

fetched live from OpenAlex

Besi (Fe) dan Seng (Zn) merupakan unsur yang berguna bagi manusia. Keberadaan logam Fe dan Zn dalam produk perikanan yang cukup kecil (trace), mudah tekontaminasi oleh kondisi lingkungan, dan metoda preparasinya yang komplek menyebabkan penentuan logam Fe dan Zn ini cukup sulit, sehingga perlu dicari suatu metoda uji yang valid dan akurat. Dalam penelitian ini dilakukan pengembangan metoda standar American of Analytical Chemistry (AOAC) tahun 2005 no. 999.10 dengan menggunakan bahan acuan bersertifikat DORM 3 (Fish Protein Certified Reference Material for Trace Metal) dari National Research Council of Canada (NRCC) untuk menguji keakuratan dan ketertelusuran hasil ke Standard Internasional (SI). Metoda ini sudah divalidasi berdasarkan parameter-parameter kimia analitik. Hasil penelitian menunjukkan rata-rata kadar Fe dan Zn dalam sampel perikanan sebesar 178 ± 14 mg.Kg-1 dan 59,8 ± 6,6 mg.Kg-1 (berat kering) dengan faktor cakupan 2 dan tingkat kepercayaan 95%, yang berada pada rentang yang ditentukan 183,5 ± 4,3 mg Kg-1 and 60 ± 1,1 mg Kg-1.Kata kunci : Fe, Zn, trace, CRM, perikanan Iron (Fe) and Zink (Zn) are essential elements for human being. Determination of this elements in fish products is quite difficult because of Fe and Zn content in trace level, easy to be contaminated by the environmental conditions, and, complex preparation methods so that it is needed to find a good and accurate method. In this paper, we have developed a standard method from American of Analytical Chemistry (AOAC), 2005, no. 999. using DORM 3 as Certified Reference Materials (CRMs) to check accuracy and traceability’s results to Standard International (SI). The method has been validated according to analytical parameters. The results showed that means of Fe and Zn concentration in the investigated fish product were 178 ± 14 mg.Kg-1 and 59.8 ± 6,6 mg.Kg-1 respectively with coverage factor 2 and 95% level of confident, and in range of expected mass were 183,5 ± 4,3 mg.Kg-1 and 60 ± 1,1 mg.Kg-1in dry basis.Keywords : Fe, Zn, trace, CRM, fisher

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.748
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.002

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 head0.008
GPT teacher head0.220
Teacher spread0.212 · 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