Mathematical Models for Plant Dispersal
Bibliographic record
Abstract
The ability of plants to move into new environments and adapt to global change depends crucially upon the dispersal of the plant seeds [2]. The probability density function describing the spatial redistribution of seeds about a parent plant (`dispersal kernel') has been the subject of intensive mathematical and biological study. Classical mathematical theory of traveling waves, nonlinear PDEs and related integral models as well as detailed biological studies have shown that it is this dispersal kernel that determines the rate at which plants can spread spatially when introduced into new environments [26], or when responding to changing environmental conditions [5]. The importance of dispersal applies equally to invasive pest plants (many of which are extremely costly to agriculture), to persistence of threatened plants and species, and to the movement of indigenous plants, such as hemlock and spruce, in response to climate change. Thus plant dispersal plays a key role in today's most pressing ecological concerns: invasive species and adaption of vegetation to global climate change and conservation biology. While invasive species in North America extract an immense ecological and economic toll (with estimated costs exceeding $130 billion US per year), the impact of costs and changes incurred by vegetation response to climate change is unclear. However, one thing is certain: in northern Canada
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".