Effects of a Cyanobacterial Toxin and Macrophyte composition on Amphibian Parasitism
Bibliographic record
Abstract
Amphibians are experiencing global declines with habitat loss and degradation, and infectious diseases as major contributors. Environmental changes such as eutrophication and climate alterations can cause the proliferation of primary producers, including cyanobacteria, and both native and invasive macrophytes. Cyanobacterial blooms can be toxic due to the production of microcystins such as MC-LR, and invasive macrophytes can alter the structural complexity of aquatic habitats – both can affect host-parasite dynamics. I examined the effects of MC-LR on larval amphibian susceptibility to infection by a trematode parasite as well as host growth and anti-parasite behaviour, finding increased susceptibility to infection at low concentrations. I also investigated how MC-LR affected the longevity and activity of trematode infectious stages (cercariae), and demonstrated variable effects among species. Lastly, I examined how environmental structural complexity, modeled as macrophyte complexity and density, affected tadpole infection by a trematode parasite and host anti-parasite behaviour but found no impact.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".