Systematic Evaluation of Biotic and Abiotic Factors in Antifungal Microorganism Screening
Bibliographic record
Abstract
Microorganisms have significant potential to control fungal contamination in various foods. However, the identification of strains that exhibit robust antifungal activity poses challenges due to highly context-dependent responses. Therefore, to fully exploit the potential of isolates as antifungal agents, it is crucial to systematically evaluate them in a variety of biotic and abiotic contexts. Here, we present an adaptable and scalable method using a robotic platform to study the properties of 1022 isolates obtained from maple sap. We tested the antifungal activity of isolates alone or in pairs on M17 + lactose (LM17), plate count agar (PCA), and sucrose–allantoin (SALN) culture media against Kluyveromyces lactis, Candida boidinii, and Saccharomyces cerevisiae. Microorganisms exhibited less often antifungal activity on SALN and PCA than LM17, suggesting that the latter is a better screening medium. We also analyzed the results of ecological interactions between pairs. Isolates that showed consistent competitive behaviors were more likely to show antifungal activity than expected by chance. However, co-culture rarely improved antifungal activity. In fact, an interaction-mediated suppression of activity was more prevalent in our dataset. These findings highlight the importance of incorporating both biotic and abiotic factors into systematic screening designs for the bioprospection of microorganisms with environmentally robust antifungal activity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".