Forensic Identification of Seal Oils Using Lipid Profiles and Statistical Models
Bibliographic record
Abstract
Seal blubber oils are used as a source of omega-3 polyunsaturated fatty acids in Canada but prohibited in the United States and (FA) European Union. Thus, a reliable method is needed to identify oils originating from seals versus fish. Two lipid profiling methods, fatty acid analysis using gas chromatography and triacylglycerol (TAG) analysis using liquid chromatography and mass spectrometry, were applied with statistical models to discriminate commercial oils and blubber samples harvested from marine fish and seals. Significant differences were observed among FA profiles, and seal samples differed from each of the fish oils (p ≤ 0.001). FA and TAG profiles were used to discriminate sample groups using a random forest classifier; all samples were classified correctly as seals versus fish using both methods. We propose a two-step method for the accurate identification of seal oils, with preliminary identification based on FA profile analysis and confirmation with TAG profiles.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".