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Record W4391326401 · doi:10.1016/j.fsiae.2024.100083

Oil Identification of harp seal and other select marine mammals

2024· article· en· W4391326401 on OpenAlexaboutno aff
Megahn H. Pinedo, Deborah A. Duffield, Dalin N. D’Alessandro, Erin Price, Edgard O. Espinoza

Bibliographic record

VenueForensic Science International Animals and Environments · 2024
Typearticle
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsHARPSeal (emblem)Identification (biology)Fur sealMarine mammalFisheryOceanographyBiologyPetroleum engineeringMarine engineeringGeologyEngineeringGeographyEcologyArchaeologyArt

Abstract

fetched live from OpenAlex

Because of the rich omega-3 fatty acids content, harp seal (Pagophilus groenlandicus) oil is a popular supplement that is packaged as pills in Canada and sold for medicinal purposes, although this practice is banned in the United States. Due to US regulations, it is important to be able to distinguish between fish oil and seal oil, but the taxonomic determination of oils provenance has been a difficult problem to solve. In this study, Direct Analysis in Real Time time-of-flight mass spectrometry (DART TOFMS) was used to analyze the chemotypes of blubber samples collected from seven species of marine mammals, including seals, sea lions, and a porpoise. Results indicated that the chemotype profiles found in negative-ion mode could be used to separate all of the species using Discriminant Analysis of Principal Components (DAPC). Consequently, this study suggests that it may be possible to identify the taxonomic source of marine mammal oils based on chemical chemotypes.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.010
GPT teacher head0.261
Teacher spread0.251 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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