Assessment of alcohol percentage test for fungal surface hydrophobicity measurement
Bibliographic record
Abstract
AIM: To determine whether assessing the penetration of solutions with different concentrations of ethanol (alcohol percentage test: APT) on fungal surfaces is effective in characterization of hydrophobicity on fungal surfaces. METHODS AND RESULTS: APT and contact angle (CA) measurements were conducted on nine hydrophobic and two hydrophilic fungal strains from the phyla of Ascomycota, Basidiomycota and Zygomycota. There was a strong positive correlation (R(2) = 0.95) between the APT and CA measurements from eight of the nine hydrophobic stains (four pathogenic and mycotoxigenic Fusarium taxa, one melanosporaceous biotrophic taxon, Alternaria sp, Penicillium aurantiogriseum and Cladosporium cladosporioides). Hydrophilic control strains, Mortierella hyalina and Laccaria laccata, had CAs <90 degrees and no measurable degree of hydrophobicity using the APT method. CONCLUSIONS: The APT method was effective in measuring the degree of hydrophobicity and can be conducted on different zones of fungal growth. SIGNIFICANCE AND IMPACT OF THE STUDY: Characterization of fungal surface hydrophobicity is important for understanding of its particular role and function in fungal morphogenesis and pathogenesis. APT is a simple method that can be utilized for fungal hydrophobicity measurements when CA cannot be measured because of obscured view from aerial mycelia growth.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".