Bibliographic record
Abstract
Cross-reactivity among tree pollens is not as pronounced as that among grass or ragweed pollens. Seasonal pollen counts found in popular media only report selected tree pollen counts and may not reflect locally prevalent pollens. Atmospheric pollen in London, Ontario was collected with a Burkard air sampler from 1999-2009. Pollen was identified with light microscopy. The average monthly pollen count as well as total was calculated from January 1999 to December 2009. The local results were compared with those reported on the Weather Network website. The Weather Network website only reports six tree pollens: alder, birch, oak, maple, elm and poplar, and do not consider regional variability in the seasons. In general, our seasons and pollen counts are in agreement with the Weather Network in terms of oak, maple and poplar pollens. However, local pollen counts detected lower quantities and shorter seasons for alder, birch and elm pollens. Large quantities of mulberry, cedar/juniper and walnut/hickory pollens were found in our local atmosphere, and not reported by the Weather Network. (see Figure 1 ) Transparent grey color represents the Weather Network pollen counts. Local counts are in color. The width of the bar represents the season, while the height represents quantity of the individual pollens. Note the small quantities of alder, birch and elm. Considerable mulberry, walnut/hickory and cedar/juniper present in our local counts were not reported by the Weather Network. Local pollen counts may differ from those reported in popular media. The clinical relevance of the difference is not yet known. Knowledge of the local plant taxonomy and allergen cross-reactivity is important in selecting clinically relevant pollens for testing and immunotherapy, especially considering pollens that do not cross-react antigenically with those in the standard tree “mix”.
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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 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".