Ecological implications for assisted migration in Canadian forests
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
Forest ecosystems are already being impacted by climate change as natural migration rates are outpaced by rapidly changing climate conditions. Human-assisted migration has been proposed as a potential management option to maintain optimal health and productivity of Canada's forests; however, a better understanding of the ecological implications is needed to inform decision-making on assisted migration (AM). This paper examines the ecological constraints and consequences of AM, and discusses options for their mitigation at three scales: translocation over long distances (assisted long-distance migration), translocation just beyond the range limit (assisted range expansion), and translocation of genotypes within the existing range (assisted population migration). From an ecological perspective, we find that AM is a feasible management option for tree species and that constraints and consequences can be minimized through careful application of available knowledge and tools.
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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) | 0.012 | 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