Cultural Management of Living Trees: An International Perspective
Bibliographic record
Abstract
Culturally modified trees, or CMTs, are a phenomenon of forest-dwelling peoples worldwide, from North America to Scandinavia, to Turkey, to Australia. Living trees from which materials are harvested (edible inner bark, pitch and resin, bark, branches), or which are modified through coppicing and pollarding to produce wood of a certain size and quality, or which are marked in some way for purposes of art, ceremony, or to indicate boundary lines or trails, all represent the potential of sustainable use and management of trees and forested regions. Often their use is associated with particular belief systems or approaches to other life forms that result in conservation of standing trees and forests, and preserving or enhancing their habitat value and productivity, even while they serve as resources for people. Various types of culturally modified trees have religious or spiritual significance, tying people to their ancestors who used the trees before them, and signifying traditional use and occupancy of a given region. Although some CMTs are legally protected to some extent in some jurisdictions, many are at risk from industrial forestry, urban expansion and clearing land for agriculture, and immense numbers of CMTs from past centuries and decades have already been destroyed. The diverse types, and the patterns of CMT creation and use, need further study; these trees, collectively, are an important part of our human heritage.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".