Historical occurrence of alien arthropods and pathogens on trees in Canada
Bibliographic record
Abstract
The Canadian Forest Invasive Alien Species (CanFIAS) database provides point records of alien arthropod (insects and mites) and pathogen (fungi) species found on trees in Canada extracted from more than 100 years of national surveys. Each record includes a species identification, location, year of observation, and host association and is linked electronically to its original source. More than 175 000 records of 329 alien arthropod species and 11 plant pathogens are available. Historical rates of detection, as indicated by first records, were greatest in the decades following the two world wars. The overall rate has been approximately three species per year since 1900. Richness of alien species is greatest in the Coastal and Great Lakes–St. Lawrence forest ecozones and lowest in the Subalpine and Tundra ecozones. The alien species most significant in terms of extent of invasion and damage to trees are tree-host specialists, feeding on or infecting mostly one or two genera in a single plant family. Important commercial trees including pine, spruce, poplar, and birch and amenity genera including willow, cherry, and maple host the greatest diversity of alien species. Sap-feeding insects are the most speciose feeding group, but foliage-feeding and wood-boring insects and plant pathogens cause the most damage.
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 | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".