Homogeneity and stability testing of a candidate reference material for the determination of total arsenic in tuna fish sample
Bibliographic record
Abstract
Total arsenic in tuna fish is usually found in relatively very low concentrations of below 10 μg/g. Two major difficulties in the measurement of total arsenic in tuna fish are the concentration and its matrix interferences. Accuracy and precision in its measurement is mandatory for an accreditated testing laboratory, therefore the availability of a suitable Reference Material (RM) is necessary. RM is a material or substance with one or more of its properties being sufficiently homogeneous and stable that are well established to be used for calibration of an apparatus, the assessment of a measurement method, or for assigning values to materials (ISO Guide 30-Ref C1). RMs are necessary in method development and validation, estimation of measurement uncertainty, internal quality control, proficiency testing and training. Both homogeneity and stability are essensial in the preparation of a RM of biological origin. The National Research Council Canada (NRC) developed Certified Reference Material (CRM) DORM-2, dogfish muscle CRM for trace metals, which was recently replaced by DORM-3. However, in Indonesia CRMs are difficult to purchase. The objective of this study is to provide a RM which can be used as an in-house reference bottle number was applied in this analysis. Statistical analysis was carried out and the uncertainty of homogeneity for total arsenic was calculated using the one-way analysis of variance (ANOVA).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".