Methods for management of soilborne plant pathogens
Bibliographic record
Abstract
Soilborne pathogens cause significant economic losses in agricultural production all over the world. These species can survive for many years in the absence of a host plant by forming persistent structures such as microsclerotia, sclerotia, chlamydospores or oospores. Consequently, soilborne diseases are particularly difficult to predict, detect, diagnose and successfully control. Over the past 30 years, a fumigant, methyl bromide, has been widely used for their control in many crops. In 1992, methyl bromide was listed as an ozone-depleting substance under the Montreal Protocol ? an international treaty to protect the ozone layer. During the phaseout of methyl bromide, problems generated in agricultural production made it clear that dependence on a single method or a single chemical should be avoided. The objective of this review paper was to summarize the current knowledge about different methods of soilborne disease control including: crop rotation, steam soil disinfection, soil amendments, hydroponics and soilless growing systems, soil solarization, grafting, biological control and use of natural compounds, and chemical control. Positive and negative aspects of all available methods were reviewed. Benefits, achieved by simultaneous application of several methods based on different mechanisms of actions, are discussed.
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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".