MétaCan
Menu
Back to cohort
Record W2945135199 · doi:10.1017/aaq.2019.29

Cultural Forests in Cross Section: Clear-Cuts Reveal 1,100 Years of Bark Harvesting on Vancouver Island, British Columbia

2019· article· en· W2945135199 on OpenAlexaffabout
Jacob Earnshaw

Bibliographic record

VenueAmerican Antiquity · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicForest Ecology and Biodiversity Studies
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsIndigenousBark (sound)GeographyArchaeologyDendrochronologyNatural (archaeology)TaphonomyPeriod (music)EcologyForestryBiology

Abstract

fetched live from OpenAlex

Culturally modified trees (CMTs) provide tangible evidence of long-term forest use by Indigenous peoples. In Northwest Coast cedar forests, this record rarely spans beyond the last three centuries because older bark-harvest scars have been obscured through taphonomic processes such as natural healing and decay. Thus, archaeological visibility and identification are hindered. Here, I recover chronologies of ancient forest harvesting using a post-impact assessment methodology of targeting old-growth clear-cuts in southern Nuu-chah-nulth territories on the west coast of Vancouver Island, British Columbia, Canada. Bark-peeling scars are identified and dated in cross section by growth-ring patterns of recently logged trees. Approximately half of all bark-peeling scars are “embedded” inside healing lobes, suggesting at least half of all such CMTs are effectively invisible in standing forests. Features in these post-impact surveys predated those discovered in conventional archaeological impact assessments by a mean of almost a century. Additionally, one of the oldest continually used cultural forests ever recorded, dating to AD 908, is found in the Toquaht Nation traditional territory. These findings uncover measurable frequencies of cedar-bark harvesting generations prior to the contact period and reveal the inadequacy of heritage protections for old-growth cedar stands.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.678
Threshold uncertainty score0.930

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.012
GPT teacher head0.232
Teacher spread0.220 · 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.

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

Citations13
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueAmerican AntiquitySame topicForest Ecology and Biodiversity StudiesFrench-language works237,207