Pioneering insights into the global and local origins of Betula spp. pollen in Iceland: Tracing long-distance transport pathways
Bibliographic record
Abstract
Iceland’s natural woodlands are dominated by the downy birch ( Betula pubescens ), while the dwarf birch ( B. nana ) is common in shrublands. These two species are the local sources of allergenic pollen that, however, may also be transported from outside Iceland (distant sources). This study aims to detect long-distance pollen transport, elucidate its mechanisms, and assess the relative contributions of local and distant sources to Iceland’s birch pollen pool. Pollen records (1998–2023) for Akureyri and Reykjavik were investigated using surface meteorological data, back-trajectories calculated by the Hybrid Single Particle Lagrangian Integrated Trajectory model (HYSPLIT) and transformed into Potential Source Contribution Function (PSCF), complemented with Sea Level Pressure (SLP) and 500 hPa geopotential height (z500) patterns. Moreover, distributions of Betula spp. were modelled using random forest models to show the location of potential birch pollen sources. We evidenced that birch pollen was transported across the Atlantic Ocean to Iceland, especially before the local pollen season, from Eastern Europe and Scotland, sometimes in large quantities (max:456 pollen m -3 ). The SPIn in Akureyri was higher when pollen transported from the eastern part of Iceland or Scandinavia overlapped with the local pollen pool. In Reykjavik, pollen was transported from northern, western Iceland, but probably also from Greenland and Labrador. Betula spp. distribution maps in Iceland can aid future species distribution modelling under climate change. This research enhances the understanding of Arctic pollen transport dynamics and highlights the need for further research on high-latitude pollen dispersion mechanisms.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".