Effects of mesophilic and thermophilic composts on suppression of Fusarium root and stem rot of greenhouse cucumber
Bibliographic record
Abstract
Three composts were tested for their ability to suppress root and stem rot caused by the soil borne fungal pathogen Fusarium oxysporum f. sp. radicis-cucumerinum (FORC) on cucumber. Two of the composts were prepared from separated dairy solids either by windrow (WDS) or vermicomposting (VMC) while the third, obtained from International Bio-Recovery (IBR), was prepared from vegetable refuse using aerobic digestion. Three sets of potting mixes were prepared by mixing the composts with sawdust at varying ratios, and seeded with cucumber cv. Corona. After 14 days of growth in the greenhouse, inoculum of FORC (20 mL of 5 x 10(6) micro-conidia per mL) was applied to each pot at three different times (14, 21, and 35 days). In unamended inoculated pots, the pathogen caused stunted growth and reduced flowers. Amendment of WDS in the potting mix suppressed these symptoms, while VMC and IBR had no effect. All three composts reduced the FORC colony forming units (cfu) at the end of the experiment (10 weeks). There was a large increase of fluorescent bacteria near the vicinity of roots particularly in WDS amended potting mixes. When water extracts of the composts were plated onto acidified potato dextrose agar (APDA), only IBR contained a potent thermostable inhibitor to FORC. This inhibitor was removed by activated charcoal but was not partitioned into petroleum ether at acid, basic, or neutral pH. Inhibition of FORC by IBR was not due to electrical conductivity or trace elements in the compost. Contrasting effectiveness of the WDS and VMC made from the same waste suggests that composting method can influence the disease suppression properties of the finished compost.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".