Application of Hydrothermal Time Models to Predict Sclerotial Germination of <i>Athelia rolfsii</i>
Bibliographic record
Abstract
Athelia rolfsii, causal agent of “southern blight” disease, is a soilborne fungal pathogen with a wide host range of more than 500 species. This study's objectives were to (i) quantify the effects of two environmental factors, temperature and soil moisture, on germination of A. rolfsii inoculum (sclerotia), which is a critical event for the onset of disease epidemics and (ii) predict the timing of sclerotial germination by applying population-based threshold-type hydrothermal time (HTT) models. We conducted in vitro germination experiments with three isolates of A. rolfsii isolated from peanuts, which were tested at five temperatures ( T), ranging from 17 to 40°C, four matric potentials (Ψ m) between −0.12 and −1.57 MPa, and two soil types (fine sand and loamy fine sand), using a factorial design. When Ψ m was maintained between −0.12 and −0.53 MPa, T from 22 to 34°C was found to be conducive to sclerotial germination (>50%). The HTT models were fitted for a range of T (22 to 34°C) and Ψ m (−0.12 to −1.57 MPa) that accounted for 84% or more of variation in the timing of sclerotial germination. The estimated base T ranged between 0 and 4.5°C and the estimated base Ψ m between −2.96 and −1.52 MPa. The results suggest that the HTT modeling approach is a suitable means of predicting the timing of A. rolfsii sclerotial germination. This HTT methodology can potentially be tested to fine-tune fungicide application timing and in-season A. rolfsii management strategies. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY 4.0 International license .
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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 | 0.001 |
| 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 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".