Cultural Forests in Cross Section: Clear-Cuts Reveal 1,100 Years of Bark Harvesting on Vancouver Island, British Columbia
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".