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Record W2482473572 · doi:10.1017/rdc.2016.46

A Marine Reservoir Effect ∆R Value for Kitandach, in Prince Rupert Harbour, British Columbia, Canada

2016· article· en· W2482473572 on OpenAlexafffundabout
Kevan Edinborough, Andrew Martindale, Gordon Cook, Kisha Supernant, Kenneth M. Ames

Bibliographic record

VenueRadiocarbon · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArchaeology and ancient environmental studies
Canadian institutionsUniversity of AlbertaUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of CambridgeNational Science Foundation
KeywordsHarbourRadiocarbon datingArchaeologyMiddenGeographyOceanographyGeology

Abstract

fetched live from OpenAlex

Abstract Prince Rupert Harbour (PRH), on the north Pacific Coast of British Columbia, contains at least 157 shell middens, of which 66 are known villages, in an area of approximately 180 km 2 . These sites span the last 9500 yr and in some cases are immense, exceeding 20,000 m 2 surface area and several meters in depth. Recent archaeological research in PRH has become increasingly reliant on radiocarbon dates from marine shell for developing chronologies. However, this is problematic as the local marine reservoir effect (MRE) remains poorly understood in the region. To account for the MRE and to better date the Harbour’s sites, we propose a ΔR of 273±38 for the PRH area, based on our work at the site of Kitandach (GbTo-34), a massive shell midden-village centrally located within the Harbour. We followed the multiple paired sample approach for samples from specific contexts and ensured contemporaneity within the groups of marine and terrestrial materials by statistically assessing for outliers using the χ 2 test. Taking together, the results for this and previous studies, it appears the MRE was fairly constant over the past 5000 yr.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.004
GPT teacher head0.168
Teacher spread0.164 · 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

Citations18
Published2016
Admission routes3
Has abstractyes

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