Bibliographic record
Abstract
Pathogens are considered a serious threat to which wild populations must adapt, most particularly under conditions of rapid environmental change. One way host adaptation has been studied is through genetic population structure at the major histocompatibility complex (MHC), a complex of adaptive genes involved in pathogen resistance in vertebrates. However, while associations between specific pathogens and MHC alleles or diversity have been documented from laboratory studies, the interaction between hosts and pathogens in the wild is more complex. As such, identifying selective agents and understanding underlying co-evolutionary mechanisms remains a major challenge. In this issue of Molecular Ecology, Evans & Neff (2009) characterized spatial and temporal variation in the bacterial parasite community infecting Chinook salmon (Oncorhynchus tshawytscha) fry from five populations in British Columbia, Canada. They used a 16S rDNA sequencing-based approach to examine the prevalence of bacterial infection in kidney and looked for associations with MHC class I and II genetic variability. The authors found a high diversity of bacteria infecting fry, albeit at low prevalence. It was reasoned that spatial variability in infection rate and bacterial community phylogenetic similarity found across populations may represent differential pathogen-mediated selection pressures. The study revealed some evidence of heterozygote advantage at MHC class II, but not class I, and preliminary associations between specific MHC alleles and bacterial infections were uncovered. This research adds an interesting perspective to the debate on host-pathogen co-evolutionary mechanisms and emphasizes the importance of considering the complexity of pathogen communities in studies of host local adaptation.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".