An Overview of Canadian Research Activities on Diseases Caused by <i>Phytophthora ramorum</i>: Results, Progress, and Challenges
Bibliographic record
Abstract
International trade and travel are the driving forces behind the spread of invasive plant pathogens around the world, and human-mediated movement of plants and plant products is now generally accepted as the primary mode of their introduction, resulting in huge disturbance to ecosystems and severe socio-economic impact. These problems are exacerbated under the present conditions of rapid climatic change. We report an overview of the Canadian research activities on Phytophthora ramorum. Since the first discovery and subsequent eradication of P. ramorum on infected ornamentals in nurseries in Vancouver, British Columbia, in 2003, a research team of Canadian government scientists representing the Canadian Forest Service, Canadian Food Inspection Agency, and Agriculture and Agri-Food Canada worked together over a 10-year period and have significantly contributed to many aspects of research and risk assessment on this pathogen. The overall objectives of the Canadian research efforts were to gain a better understanding of the molecular diagnostics of P. ramorum, its biology, host-pathogen interactions, and management options. With this information, it was possible to develop pest risk assessments and evaluate the environmental and economic impact and future research needs and challenges relevant to P. ramorum and other emerging forest Phytophthora spp.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".