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Record W2292169105 · doi:10.14430/arctic4546

Identification of Bird Species Used to Make a Viking Age Feather Pillow

2016· article· en· W2292169105 on OpenAlexvenueno aff
Carla J. Dove, Stephen Wickler

Bibliographic record

VenueARCTIC · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsnot available
FundersSmithsonian LibrariesFederal Aviation AdministrationHelsingin YliopistoSmithsonian Institution
KeywordsEiderFeatherCharadriiformesCormorantFlight featherHeronAythyaGeographyWaterfowlAnseriformesSternaZoologyEcologyPlumageBiologyPredation

Abstract

fetched live from OpenAlex

A grave containing the remains of a wooden boat was discovered in 1934 under a low mound in a bog at Øksnes in the Vesterålen islands of northern Norway. The boat grave dates to the 10th century in the Viking Age, and grave goods placed in the boat include an iron axe, a cowhide in which the body was wrapped, and pillow remains consisting of feather stuffing and a wool textile cover. A microscopic analysis of the feathers from a subsample of the pillow fill identified three avian orders: Anseriformes (eider); Suliformes (cormorant), and Charadriiformes (unspecified gull). It was possible to make one species-level identification of Great Cormorant (<em>Phalacrocorax carbo</em>) and to narrow the gull types to the “white-headed” gull group. The sample was composed of a nearly equal mix of downy and pennaceous feather types. Downy feathers from gulls (Laridae) composed the majority of the material in this sample. While it is reported that feathers and down (assumed to be eider) were used in the Late Iron Age, this is the first successful attempt to identify bird species used in these materials and suggests that avian species identifications should be explored in other such burial items to enhance our understanding of human-wildlife interactions throughout Norse history.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.239
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.021
GPT teacher head0.228
Teacher spread0.207 · 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; both teacher heads agree on what is shown here.

Study designObservational
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

Citations15
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueARCTICSame topicWildlife Ecology and ConservationFrench-language works237,207