Cities as hot stepping stones for tree migration
Bibliographic record
Abstract
Cities around the world are increasingly encouraging and promoting tree-planting initiatives to sequester carbon and mitigate climate change. Picking the right tree in the right place is essential for maintaining the sustainability of urban landscapes 1 , 2 . As a response to recent climate warming, cities such as Philadelphia, Chicago, and London (Ontario, Canada) have already begun planting more southerly tree species on urban parks, streets, as well as other municipal lands. However, the potential of urban tree planting to assist species migration in a wider landscape has often been overlooked 3 . Due to the urban heat island effect, cities are experiencing a preview of future climates for nearby rural areas, potentially offering a climatic condition suitable for the persistence of outlier populations at higher latitudes than their native ranges. The outlier populations in cities could serve as propagule sources for species’ poleward migration under climate change. Moreover, since trees can cool their environment, planting trees in cities can slow the rate of warming, which in turn allows them to grow for decades to reach reproductive maturity for further expansion. Here, we discuss the potential of urban tree plantings to assist the poleward migration of forest trees in temperate and boreal regions. Emphasis is placed on the unique climatic condition that cities could provide for the establishment, growth, and expansion of outlier populations.
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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 | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".