Bibliographic record
Abstract
The seemingly ubiquitous presence of gregarine parasites in numerous mosquito taxa (Table 1; Chen 1999) suggests that consideration of these parasites as biological control agents may be a worthwhile endeavor. However, because most studies prior to 1985 did not demonstrate significant fitness (e.g., mortality, adult size) differences between infected and non-infected mosquitoes, Beier and Craig (1985) deemed in their overview of gregarine parasites of mosquitoes that ‘‘There is no evidence that gregarines can be used to control mosquitoes, and no evidence that gregarines in their natural habitat have a significant negative impact on populations of their normal host.’’ (p 182) However, the authors did suggest that because ‘‘conventional strategies for controlling container-breeding mosquitoes are not effective, the possibility of using gregarines in unnatural mosquito hosts should not be ruled out.’’ A number of additional studies have since been published that have examined the pathogenicity of gregarine parasites both for natural and nonnatural hosts, as well as for hosts reared in stressful vs. non-stressful environments. This paper reviews the outcomes of these studies and addresses whether the prospects of using gregarines as mosquito biological control agents have changed with these recent findings.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".