Bibliographic record
Abstract
The role of biodiversity as it affects the control of soil-borne fungal pathogens is discussed. Soil-borne fungal plant pathogens have often proven difficult to manage with conventional methods of disease control. Nonetheless, researchers have characterized several naturally occurring “disease-suppressive” soils where crop loss from disease is less than would otherwise be expected. Suppressive soils can also result from the incorporation of various amendments into soil. In most cases, disease control in such soils has been shown to be biological in nature; that is, soil organisms appear to directly or indirectly inhibit the development of disease. Increased knowledge of the identity and functioning of these organisms may support the development of techniques that can be used to develop suppressiveness in soils that are otherwise disease-conducive. Populations of pathogens themselves have been shown to exhibit considerable genetic diversity; the ability of populations to respond to disease control measures should be considered when developing a management strategy. New molecular techniques can be exploited to better characterize soil communities, including the pathogens themselves, as well as community responses to various disease control options. The contributions of Canadian researchers to these areas are discussed and models for further study are proposed. Key words: Biocontrol, molecular technologies, functional diversity, integrated pest management
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".