B<scp>IOLOGICAL</scp> C<scp>ONTROL IN</scp> G<scp>REENHOUSE</scp> S<scp>YSTEMS</scp>
Bibliographic record
Abstract
The controlled environment of greenhouses, the high value of the crops, and the limited number of registered fungicides offer a unique niche for the biological control of plant diseases. During the past ten years, over 80 biocontrol products have been marketed worldwide. A large percentage of these have been developed for greenhouse crops. Products to control soilborne pathogens such as Sclerotinia, Pythium, Rhizoctonia and Fusarium include Coniothyrium minitans, species of Gliocladium, Trichoderma, Streptomyces, and Bacillus, and nonpathogenic Fusarium. Products containing Trichoderma, Ampelomyces quisqualis, Bacillus, and Ulocladium are being developed to control the primary foliar diseases, Botrytis and powdery mildew. The development of Pseudomonas for the control of Pythium diseases in hydroponics and Pseudozyma flocculosa for the control of powdery mildew by two Canadian research programs is presented. In the future, biological control of diseases in greenhouses could predominate over chemical pesticides, in the same way that biological control of greenhouse insects predominates in the United Kingdom. The limitations in formulation, registration, and commercialization are discussed, along with suggested future research priorities.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.121 | 0.093 |
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".