Rapid Removal of Symptomatic Trees Reduces Dutch Elm Disease Infection Rates
Bibliographic record
Abstract
In the province of Manitoba, Canada, 14 communities were paired on the basis of size and location. From 2004 to 2010 in one member of each pair, rapid removal of American elm (Ulmus americana) trees that displayed symptoms of Dutch elm disease was practiced: newly symptomatic trees were removed within six weeks of symptom detection. The remaining member of each pair continued with autumn or winter removal of symptomatic trees. Treed urban study areas were selected in each community, and in 2008 an inventory of U. americana was taken in these areas. From this inventory and from records of tree removals, estimates of the number of living U. americana and prevalence of Dutch elm disease were made for each year from 2004 to 2010. Following the switch to rapid removal, the annual prevalence of Dutch elm disease in rapid removal communities was 1.5 ± 0.2%, significantly lower than in communities with autumn/winter removal (3.1 ± 0.4%). The study authors estimate that in similar areas the value of the elm resource after 10 years would be almost CAD $600,000/km2 greater if rapid removal rather than autumn/winter removal were practiced. Therefore, under conditions similar to those in these communities, rapid removal should be a component of management programs for Dutch elm disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".