Fred Brauer (University of British Columbia),
Bibliographic record
Abstract
(2009). In contrast to these schools, which focused on the mathematics of epidemiology and public health, this school focused on the dynamics of invasions and evolution. The school was organized jointly by two MITACS research groups: a group of researchers working on mathematical models of infectious diseases (www.liam.yorku.ca/research/MADI/) and a group of researchers working on mathematical models of biological invasions and dispersal (www.unb.ca/bid). Historically, the fields of mathematical ecology and the dynamics of evolution have developed separately and it is only recently that work has been done to begin to bridge these two fields. Models for the evolution of populations assumed slowly changing or constant populations, and models for ecological populations assumed evolution took a much longer time scale than population dynamics. Recent theoretical work has begun to bridge these two approaches allowing population traits to change on the same timescale as population size. This advance is necessary for a theoretical framework for pathogen evolution in many systems. The influenza virus provides a pressing example. The timescale of viral evolution is similar to the rate of spread of the virus though the host population. Any control measures, such as vaccines or antiviral medications, must take into account the rapid appearance of drug resistant strains. Other examples presented in lectures include weedy species [7], HIV and vector-borne parasites such as malaria.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.421 | 0.172 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".