Attractions of nematodes to yeasts are influenced by both nematodes and yeasts
Bibliographic record
Abstract
Both yeasts and nematodes are significant components of the soil biomass and biodiversity and fulfil a wide variety of ecological functions. However, relatively little is known about the interactions between yeasts and nematodes, including the potential use of yeasts by nematodes as a food source and potential diseases that these yeasts can cause in nematodes. To begin investigating their ecological relationships, we tested the in vitro attractive ability of representative yeast species on nematodes. A total of 15 yeast strains belonging to six species were assayed for their attraction abilities towards two nematode species. Our results suggest that nematodes are able to distinguish between their microbial food source and yeast pathogens. Furthermore, our analyses demonstrated that host nematodes, yeast species, and in some cases yeast strains all contributed to the variation in attraction abilities. We hypothesize that volatile and/or diffusible organic compounds released from the yeasts are involved in attracting the nematodes. These results suggest the attraction and consumption interaction between soil yeasts and nematodes may be common in the environment. These interactions may be significant in regulating the populations of both the yeasts and their nematode hosts in natural soil ecosystems. The data presented here could also help to develop nematode-based model systems for studying fungal pathogenesis.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".