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Record W2980760693

Ts’a7inwa (gooseneck barnacles) as a proxy for archaeological efforts to understand shellfish as food in Nuu-chah-nulth territories

2019· dissertation· en· W2980760693 on OpenAlexaboutno aff
Meaghan Efford

Bibliographic record

VenueUVic’s Research and Learning Repository (University of Victoria) · 2019
Typedissertation
Languageen
FieldArts and Humanities
TopicAncient Egypt and Archaeology
Canadian institutionsnot available
Fundersnot available
KeywordsShellfishFisheryArchaeologyGeographyProxy (statistics)BiologyFish <Actinopterygii>Aquatic animalComputer science
DOInot available

Abstract

fetched live from OpenAlex

This thesis examines the comparative abundance of shellfish from archaeological assemblages on the west coast of Vancouver Island in Nuu-chah-nulth territories. Eighteen sites spanning the Nuu-chah-nulth region emphasize the diversity in invertebrate foods that have been consumed 5000-150 years ago: Yaksis Cave, Loon Cave, and Hesquiat Village at Hesquiat Harbour; Chesterman Beach; Spring Cove; Ts’ishaa, Ch’ituukwachisht (North and South), Tl’ihuuw’a, Shiwitis, Huumuuwaa, Maktl7ii, Huts’atswilh, Kakmakimilh, Kiix7iin, and Huu7ii. Invertebrate zooarchaeology is an understudied field that has the potential to impact ecological restoration and conservation efforts. Ubiquity, or frequency of occurrence, provides a measure of abundance for a target taxa or species through a percent presence/absence approach. Regionally conventional methods of invertebrate analysis, including weight-based quantification, primarily favour heavy and robust bivalves, such as clams and mussels, and diminish the presence of other frequently occurring invertebrates. Ubiquity-based quantification shows how frequently ‘other’ shellfish have been utilized over time and across archaeological deposits. Gooseneck barnacles (Pollicipes polymerus) are often considered rare, an unimportant intertidal resource, but ubiquity-based analyses show that they are far more abundant than previously appreciated. A methodological combination of these two approaches shows vastly different perspectives on shellfish abundance, and this has implications for how the dietary role of shellfish is understood and discussed in archaeological discourse.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.606
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.033
GPT teacher head0.273
Teacher spread0.240 · 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 teacher head, not a consensus.

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

Citations1
Published2019
Admission routes1
Has abstractyes

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