Napâttuit: Wood use by Labrador Inuit and its impact on the forest landscape
Bibliographic record
Abstract
In Nunatsiavut, recent studies have shown that major changes to forest tundra ecosystems have occurred over the past two centuries, including a shift in the abundance and range of tree/shrub species. Although this trend could be due to the highly variable climate of this period, we should also consider anthropogenic factors, such as wood harvesting, when conducting ecological studies of forest dynamics. Based on a literature review, interviews, and field observations, this article documents the interactions between residents of Nain (Nunatsiavut) and the forest landscape since the late 18th century. Nain is one of the few Inuit communities south of the tree line, and its inhabitants seem to have had an ambivalent and changing relationship with their forest landscape. Thus, though probably perceived initially as potentially dangerous, the forest has gradually been integrated into land use patterns and helped shape some aspects of Labrador Inuit culture. For Nain’s inhabitants, wood use has been continuous but not homogenous over time. Patterns of use and harvesting have changed with the socio-economic setting and have left their traces on the region’s forest stands, as is evident from the abundance of cut stumps and the scarcity of naturally dead trees.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".