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Record W2751801599 · doi:10.1371/journal.pone.0183053

Molecular markers in keratins from Mysticeti whales for species identification of baleen in museum and archaeological collections

2017· article· en· W2751801599 on OpenAlexaboutno aff
Caroline Solazzo, William W. Fitzhugh, Susan A. Kaplan, Charles W. Potter, Jolon M. Dyer

Bibliographic record

VenuePLoS ONE · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsnot available
FundersSmithsonian Institution
KeywordsBaleenArchaeologySpecies identificationIdentification (biology)BiologyZoologyGeographyFisheryWhaleEcology

Abstract

fetched live from OpenAlex

Baleen has been harvested by indigenous people for thousands of years, as well as collected by whalers as an additional product of commercial whaling in modern times. Baleen refers to the food-filtering system of Mysticeti whales; a full baleen rack consists of dozens of plates of a tough and flexible keratinous material that terminate in bristles. Due to its properties, baleen was a valuable raw material used in a wide range of artefacts, from implements to clothing. Baleen is not widely used today, however, analyses of this biomolecular tissue have the potential to contribute to conservation efforts, studies of genetic diversity and a better understanding of the exploitation and use of Mysticeti whales in past and recent times. Fortunately, baleen is present in abundance in museum natural history collections. However, it is often difficult or impossible to make a species identification of manufactured or old baleen. Here, we propose a new tool for biomolecular identification of baleen based on its main structural component alpha-keratin (the same protein that makes up hair and fingernails). With the exception of minke whales, alpha-keratin sequences are not yet known for baleen whales. We therefore used peptide mass fingerprinting to determine peptidic profiles in well documented baleen and evaluated the possibility of using this technique to differentiate species in baleen samples that are not adequately identified or are unidentified. We examined baleen from ten different species of whales and determined molecular markers for each species, including species-specific markers. In the case of the Bryde's whales, differences between specimens suggest distinct species or sub-species, consistent with the complex phylogeny of the species. Finally, the methodology was applied to 29 fragments of baleen excavated from archaeological sites in Labrador, Canada (representing 1500 years of whale use by prehistoric people), demonstrating a dominance of bowhead whale (Balaena mysticetus) in the archaeological assemblage and the successful application of the peptide mass fingerprinting technique to identify the species of whale in unidentified and partially degraded samples.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.054
GPT teacher head0.243
Teacher spread0.189 · 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

Citations41
Published2017
Admission routes1
Has abstractyes

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