Yak Etinin Coğrafi Kökenini Doğrulamak Amacıyla Mineral Elementlerin Kullanılması
Bibliographic record
Abstract
Labeling systems for niche market food products is becoming increasingly important to address consumers' expectations. Yak meat is 'green' product from natural extensive rangeland on Qinghai-Tibetan plateau. A trace technique is essential for consumers to know the origin of yak meat. In the current study, mineral fingerprints were investigated for their potential to classify yak meat according to geographical origin. The concentration of more than 50 mineral contents in 24 yak meat samples from three regions on Qinghai-Tibetan plateau were analyzed by ICP-MS. Multivariate statistical analyses were used to identify the most relevant indicators of origin. Seven elements (Na, As, Ni, Se, Rb, Cd and Ti) were selected for further routine analyses based on the significant origin differences (P<0.05). The three minerals (Se, Rb, Ti) were selected by statistics analysis and established discriminant model for yak meat traceability. Linear discriminate analysis gave an overall correct classification rate of 91.7% and cross-validation rate of 87.5%. These results demonstrate the usefulness of multi-element fingerprints as indicators for authenticating the geographical origin of yak meat.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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.
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; both teacher heads agree on what is shown here.
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".