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Record W2099795302 · doi:10.7202/1015953ar

Wood use and kayak construction: Material selection from the perspective of carpentry

2013· article· en· W2099795302 on OpenAlexaffvenue
Matthew Walls

Bibliographic record

VenueÉtudes/Inuit/Studies · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArchaeology and ancient environmental studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCarpentryArchitectural engineeringArcticThe arcticEngineeringPerspective (graphical)Variety (cybernetics)Civil engineeringComputer scienceEcologyGeologyArtificial intelligenceBiologyOceanography

Abstract

fetched live from OpenAlex

Although the natural availability and quality of wood is variable across the Arctic, there is great continuity in how it was traditionally used. This article considers the value of wood to Arctic peoples and the criteria that would distinguish the utility of different pieces. The topic is explored in the case of kayak construction, one of the most complex carpentry tasks that can be inferred from many archaeological sites. Numerous types of kayaks were built in several periods by a variety of peoples using very different toolkits. Using both ethnographic and archaeological examples, it is shown that this technology everywhere shared several key stages of construction. Within these stages, specific carpentry tasks defined the criteria that all kayak builders used to select wood. By exploring the value of wood to Arctic peoples for carpentry, this article demonstrates the potential for understanding wood use through experimental archaeology.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0070.014
Scholarly communication0.0050.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.021
GPT teacher head0.217
Teacher spread0.197 · 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 designQualitative
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

Citations6
Published2013
Admission routes2
Has abstractyes

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