Defining important canine zoonotic pathogens within the Prairie Provinces of Canada.
Bibliographic record
Abstract
The goal of this study was to establish a short list of zoonotic pathogens involving the domestic dog that can be prioritized for a companion animal surveillance program specific to the Prairie Provinces of Canada. A list of pathogens documented in dogs was created through a comprehensive review of infectious disease textbooks for the following taxonomical categories: bacteria, ectoparasites, fungi, helminths, protozoa, rickettsia, and viruses. This created an initial list of 594 pathogens that was then pared down through an extensive review of the literature using the following criteria: i) the pathogen is zoonotic/sapronotic/anthroponotic; ii) the dog is involved in transmission to humans, maintenance, or detection of the pathogen; and iii) there is a level of risk for occurrence of the pathogen in Canada. This process yielded a final list of 84 pathogens and 3 supplementary lists of canine zoonotic/sapronotic/anthroponotic pathogens that may become relevant to future surveillance programs.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".