Irrigation Quantity Effects on Anthracnose Disease of Annual Bluegrass
Bibliographic record
Abstract
ABSTRACT Irrigation can influence both turf vigor and playability of putting greens. Anthracnose (Colletotrichum cereale Manns sensu lato Crouch, Clarke, and Hillman) has become an increasingly destructive disease of annual bluegrass (ABG) [Poa annua L. f. reptans (Hausskn.) T. Koyama] putting greens, particularly when turf is under stress. This 3‐yr field study evaluated the effects of irrigation quantity (100, 80, 60, and 40% of reference evapotranspiration [ETo]) on anthracnose severity of ABG mowed daily to 3.2 mm. Severe drought stress (40% ETo) increased anthracnose severity in 2006, 2007, and 2008. Anthracnose was less severe under 60% ETo irrigation, and irrigating at 80% ETo reduced severity compared to 60% ETo Irrigating at 100% ETo initially reduced anthracnose severity compared to 40% ETo; however, 100% ETo resulted in similar disease severity later in the 2006 and 2008 seasons. While this response was not observed late in the 2007 season, plots maintained at 100% ETo had turf quality similar to plots irrigated at 40% ETo later in each year due in part to increased algal development. Irrigation to replace 80% ETo typically resulted in the least amount of disease and the best turf quality throughout the trial. Thus, irrigation to minimize drought stress while also avoiding continuous high soil water content is beneficial in reducing anthracnose and maintaining acceptable turf performance.
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".