Editorial: Forest assisted migration
Bibliographic record
Abstract
Recent research indicates that Forest Assisted Migration (FAM) may help mitigate climate change impacts on forests, with nearly 60% of studies supporting its use (Xu & Prescott, 2024). However, FAM poses risks, such as introducing invasive species and maladaptation (Chen et al., 2021), increasing susceptibility to pathogens (Grady et al., 2015), and raising social concerns (Hagerman & Kozak, 2021). This research topic considers the application of assisted migration practices to forest management. We have curated contributions from sixty-five authors studying twenty-five species across eleven articles grouped into four subtopics. All articles were published in a special Frontiers in Forests and Global Change issue titled "Forest Assisted Migration." This collection highlights the interdisciplinary nature of the research. The subtopics include:1. Genetic and environmental factors influencing plant traits 2. Assisted migration practices through field trials and silvicultural methods 3. Social attitudes toward FAM and its implications for forest planning 4. Models for improving the accuracy of seed transfer and species selection. Some contributions cross multiple subtopics. Individually and collectively, this collection substantially enhances our understanding of FAM's application.The establishment of provenance trials and common gardens are used to test tree species' responses and performance, often involving transfers across latitudinal or elevational gradients.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.013 | 0.014 |
| Insufficient payload (model declined to judge) | 0.029 | 0.016 |
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".