Bibliographic record
Abstract
The need for effective strategies in resistance management is becoming more pressing as the number of insecticide-resistant species continues to increase worldwide while insecticide resources are diminishing. Prospects for development of such strategies are enhanced by recent advances in knowledge on the biochemistry, molecular genetics, ecology, dynamics, monitoring, and other important aspects of resistance. The generally recognized approaches to resistance management are grouped under three principal categories: first, low selection pressure, supplemented by a strong component of non-chemical measures (management by moderation); second, elimination of the selective advantage of resistant individuals by increasing insecticide uptake through the use of attractants, or by suppressing of detoxication enzymes through the use of synergists (management by saturation); and third, application of multi-directional selection by means of mixtures or rotations of unrelated insecticides or by use of chemicals with multi-site action (management by multiple attack). These approaches are not mutually exclusive and elements from each can be used to formulate a season-long management program. The strategy chosen must be based on a thorough knowledge of the resistance implications of the candidate insecticides and of the biology and ecology of the species concerned, and must make use of all available non-chemical control measures.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".