Adopting a Root: Documenting Ecological and Cultural Signatures of Plant Translocations in Northwestern North America
Bibliographic record
Abstract
ABSTRACT In recent years, there has been an increasing recognition of the role humans play in the structure, composition, and function of ecosystems. Ethnoecological documentation of traditional management systems of Indigenous Peoples in northwestern North America has contributed significantly to this rethinking. A less well‐recognized but foundational part of traditional management of this region is the practice of transplanting plants and plant propagules to expand the range and accessibility of culturally valued plants. In part, the lack of recognition of such translocations has to do with difficulties in identifying evidence of such actions from the past. Here, we summarize various lines of evidence, including that from ethnographic and ethnohistoric records, languages, oral traditions, phytogeography, and archaeology, to document the widespread and long‐standing extent of plant translocation practices among Indigenous Peoples of northwestern North America. Furthermore, we demonstrate how such practices have helped shape contemporary native plant communities throughout the region. Recognizing these past contributions to current ecological contexts honors Indigenous heritage and Indigenous Peoples’ long‐term relationships with their biological worlds. [translocation, transplanting, ethnobotany, traditional resource management, northwestern North America]
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".