Accuracy and precision of population estimates of <i>Verticillium dahliae</i> on growth media in quantitative soil assays
Bibliographic record
Abstract
Verticillium dahliae Kleb. is a serious pathogen of many plant species. Growth media used to measure population density of V. dahliae in soil were evaluated for high recovery of the pathogen, as well as accuracy and precision of population estimates from naturally and artificially infested sandy loam soil using the soil dilution method. Recovery of V. dahliae from naturally infested field soil was highest on soil pectate Tergitol agar (SPT), soil extract agar + sodium polypectate (SEAP), modified pectate agar (MPA), potato dextrose agar + streptomycin sulphate (PDAS), and Talboys' prune lactose agar (TPA); however, PDAS and TPA were overgrown with contaminating fungi, making enumeration difficult. Use of SPT medium resulted in the most precise population estimate with a standard error (SE) of 12.6% of the mean followed by use of pectate agar (PA) (SE = 14.8%) and SEAP (SE = 19.1%). Ethanol, biotin, and Dox salts enhanced recovery of V. dahliae from naturally infested soil, but combining Dox salts with ethanol and biotin significantly reduced population density. Soil extract had no significant effects on population density. Accuracy of V. dahliae population estimates from sterile artificially inoculated soil was highest on modified soil extract agar (MSEA) (64%) followed by SPT (58%). However, accuracy of V. dahliae population estimates from nonsterile artificially inoculated soil was highest on SPT (36%). Soil extract is not an essential ingredient and biotin may increase recovery of V. dahliae from soil.Key words: Verticillium dahliae, verticillium wilt, population density, recovery, accuracy, precision.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".