Classification of Geographical Origin by PNN Analysis of Fatty Acid Data and Level of Contaminants in Oils From Peruvian Anchovy
Bibliographic record
Abstract
Abstract The aim of this study was to examine Peruvian anchovy oil fatty acid (FA) compositions, and to test the possibility of using the FA data to classify the oils according to geographical origin along the Peruvian coast. The levels of contaminants in a representative set of samples were determined to examine the general levels and investigate if such measurements could aid in future discrimination between oils. The FA results showed that the two known stocks of Peruvian anchovy displayed different levels of docosahexaenoic acid (DHA, 22:6n‐3) (southern stock; 14.4 ± 0.8% versus central‐northern stock; 9.9 ± 1.2%). However, principal component analysis (PCA) of the FA data indicated clusters according to three regions; North, Center and South. Using a data set of 57 anchovy samples and 21 FA as input, a probabilistic neural network (PNN) was constructed. For the validation data sets, “North” oils was predicted accurately 100% of the time, “Center” oils 100% and “South” oils 83% of the time. The levels of contaminants in the oils determined were low in all but one sample.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".