Altered pesticide use on transgenic crops and the associated general impact from an environmental perspective
Bibliographic record
Abstract
The large-scale commercial cultivation of transgenic crops has undergone a steady increase since their introduction 10 years ago. Most of these crops bear introduced traits that are of agronomic importance, such as herbicide or insect resistance. These traits are likely to impact upon the use of pesticides on these crops, as well as the pesticide market as a whole. Organizations like USDA-ERS and NCFAP monitor the changes in crop pest management associated with the adoption of transgenic crops. As part of an IUPAC project on this topic, recent data are reviewed regarding the alterations in pesticide use that have been observed in practice. Most results indicate a decrease in the amounts of active ingredients applied to transgenic crops compared with conventional crops. In addition, a generic environmental indicator -- the environmental impact quotient (EIQ) -- has been applied by these authors and others to estimate the environmental consequences of the altered pesticide use on transgenic crops. The results show that the predicted environmental impact decreases in transgenic crops. With the advent of new types of agronomic trait and crops that have been genetically modified, it is useful to take also their potential environmental impacts into account.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".