Bibliographic record
Abstract
Abstract Lentil is an old-world legume and grown in more than 70 countries. It is a major source of protein in plant-based diets and is often used to fix nitrogen in the soil as a rotational crop, especially with cereal crops. Canada, USA, and Australia are the major exporters of lentil. Around the globe, this crop faces various biotic and abiotic stresses. More than 35 insect pests and the same number of diseases are reported to infest lentil in different parts of the world. While the status of insect pests and diseases varies in different geographical regions, some of them are aphids, armyworm, cutworm, pod borer, Stemphylium blight, fusarium wilt, Alternaria blight, and rust. Cultural management strategies for abiotic and biotic stresses include crop rotation, the timing of seeding, appropriate seed rates, and weed management. Biological control agents are also known for several insect pests. Environmental-friendly options such as biopesticides and microbials (entomopathogenic bacteria, fungi and nematodes, neem products, and Trichoderma sp.) can be used as seed treatment and foliar application. Various tolerant and resistant lentil varieties are available around the globe. In the present article, we provide an IPM package for the management of major biotic stresses for lentil crop.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.069 | 0.024 |
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".