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Record W2907735296

Appendix D: Invertebrate Fauna Analysis – Huu7ii Village, Diana Island, Barkley Sound

2017· article· en· W2907735296 on OpenAlexaboutno aff
Ian D. Sumpter

Bibliographic record

VenueSFU Archaeology Press · 2017
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsFaunaArchaeologyGeographyExcavationSound (geography)PaleoecologyTaphonomyInvertebrateEcologyHistoryGeologyOceanographyBiology
DOInot available

Abstract

fetched live from OpenAlex

This volume presents the results of a collaborative project with the Huu-ay-aht First Nation, a Nuu-chah-nulth group near Bamfield on Vancouver Island’s west coast. It reviews ethnographic and ethnohistoric data on Huu-ay-aht territory and provides detailed descriptions and analysis of excavated materials from the site of Huu7ii, an ancient Huu-ay-aht village with an occupation span of nearly 5,000 years. The major focus is on the excavation of one very large house, argued to be a chiefly residence. Appendices present specific contributions to the research by Gay Frederick (vertebrate faunal analysis), Iain McKechnie (fish remains from the column samples), Ursula Arndt and Dongya Yang (aDNA of cetacean remains), Ian Sumpter (invertebrate faunal analysis), Beth Weathers (paleoethnobotany), and Marlow Pellatt (paleoecology and the pollen record). The results make a significant contribution to our knowledge of the Nuu-chah-nulth past and to household archaeology on the Northwest Coast.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.883
Threshold uncertainty score0.782

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.007
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.2340.044

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 head0.046
GPT teacher head0.374
Teacher spread0.329 · 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 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

Citations0
Published2017
Admission routes1
Has abstractyes

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