Using Traditional Ecological Knowledge to Understand the Diversity and Abundance of Culturally Important Trees
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Combining Indigenous traditional ecological knowledge (TEK) with scientific research holds promise for more effectively meeting community objectives for the conservation of cultural forest resources. Our study focuses on predicting the abundance of western redcedar trees ( Thuja plicata) within the traditional territories of five Indigenous Nations that are part of the Nanwakolas Council in British Columbia, Canada. Indigenous people in this region use western redcedar extensively for cultural practices, such as carving dugout canoes, totem poles, and traditional buildings. However, after more than a century of industrial logging, the abundance of redcedar suitable for these types of practices is in decline and no longer reflects past baseline conditions. We assess how using TEK from interviews with Indigenous carvers refines predictions of resource abundance compared to using only conventional field surveys. Our findings reveal that western redcedar trees suitable for traditional carving are generally rare, and that some important growth forms, such as those associated with carving community canoes, are nearly extirpated from the landscape. We demonstrate a useful application of TEK in conservation planning and highlight concerns about the impact of industrial forestry on culturally important trees.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it