Fast, slow, and adaptive management of habitat modification–invasion interactions: woodland caribou (<i>Rangifer tarandus</i>)
Bibliographic record
Abstract
Abstract Woodland caribou ( Rangifer tarandus ) are declining throughout much of their North American range. In Alberta, industrial development is the major driver for the disturbance to caribou habitat and ecosystem dynamics involving the invasion of other ungulates and predators. Non‐linear predation increases the chances of extirpation. Reversing the decline of an individual herd requires some combination of habitat protection, slow habitat restoration, and control of the faster‐to‐adjust invading ungulates and/or predators. To explain the ecosystem dynamics affecting the decline, and its potential reversal, we develop a mathematical ecological model that incorporates the interactive effects within and between trophic levels, between slow and fast ecosystem variables, and includes non‐linearity. The most effective mix of fast variable controls depends on the relationships among caribou, predators, and their primary prey. Uncertainties create challenges. Even if fast variable controls improve caribou numbers, habitat restoration is necessary to permanently reverse the decline of an individual herd. However, the outcomes of habitat restoration actions are highly uncertain. Uncertainty is addressed at the provincial scale by combining the model with active adaptive management, and the closely related real options approach, which both guarantees against the worst outcome of provincial extirpation and allows further development if restoration activities are successful.
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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) | 0.001 | 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 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".