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Record W7139286277

Some Like it Hot: Exploring the Archaeobotany Of Roasting Features in Southern British Columbia

2018· other· W7139286277 on OpenAlexaboutno aff
Natasha Lyons, Anna Marie Prentiss, Sandra Peacock, Bill Angelbeck

Bibliographic record

VenueSummit (Simon Fraser University) · 2018
Typeother
Language
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsPaleoethnobotanyRoastingAssemblage (archaeology)Prospection
DOInot available

Abstract

fetched live from OpenAlex

Roasting features, also known as earth ovens, have been used by First Nations Peoples since the late Holocene to cook food for both immediate consumption and winter storage.Across southern British Columbia, earth ovens built by Salish communities in low-and mid-elevation meadows and riverine villages were part of carefully coordinated, multi-layered annual patterns of movement within the landscape to harvest and produce food.In this paper, we examine the patterning of floral-and to a lesser extent, faunal-data from earth oven complexes located in four village and four upland sites, finding differences between assemblages that appear to relate to the nature and diversity of use between site types.Our preliminary results support the contention that earth ovens in village contexts were used in more ways, and potentially by a wider array of cooks, than those in upland contexts.This analysis forms a first step towards a broader and more detailed study of ancient plant production as rendered through the lens of earth ovens in upland and lowland settings across southern British Columbia.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0090.002
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.001

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 head0.020
GPT teacher head0.203
Teacher spread0.183 · 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
Published2018
Admission routes1
Has abstractno

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