Indigenous knowledge, forest landscape modeling, and the cumulative effects of environmental changes
Bibliographic record
Abstract
Boreal landscapes are exposed to climate change, forestry, and other industrial stressors with consequences for Indigenous people's wellbeing and relationship with traditional lands. As a collaborative and transdisciplinary research team including researchers and students from universities, Indigenous communities, and government and non-government organizations, we addressed the consequences of these environmental changes for an Eeyou (Cree) community and an Anishnaabe community in Eastern boreal Canada (Quebec). Our aims were to (1) develop a landscape value model combining qualitative and quantitative data as well as knowledge from Indigenous land-use experts and forest landscape simulations; (2) evaluate the vulnerability of Indigenous landscape values to environmental changes; and (3) assess the relative importance of climate change and forestry as drivers of environmental changes. We developed a set of 12 variables based on interviews with Indigenous experts, describing four dimensions of landscape value: abundance, quality, access, and experience. We then performed forest landscape simulations (2000-2100) with the model LANDIS-II using scenarios combining climate change and forestry gradients. We presented the simulation outputs to Indigenous experts and elicited the probability of fulfilling their needs in the future. We combined Indigenous knowledge and forest landscape simulations within a probabilistic model (Bayesian network). The projections indicate that rapid and acute changes in forest structure and composition are to be expected. The most vulnerable values are those associated with mature and undisturbed forests and include the ability to trap, hunt moose, and recover health and energy from the land (ressourcement in French, KOKi OTAPiNAN NOPiMiK iNATiSi8iN in Anishnaabemowin). Timber harvesting rate influences the timing and amplitude of change. The influence of climate change varied from one region to another and was mostly associated with wildfire frequency. Adaptation strategies may include reducing timber harvesting rates, implementing wildfire-prevention measures, and valuing alternative forest uses.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".