Bibliographic record
Abstract
The Industry responsible for the discovery and development of crop protection compounds has undergone dramatic changes and increasing consolidation since the initial innovations in synthetic organic fungicides, herbicides and insecticides in the late 1940s and early 1950s. Likewise, there have been striking changes in the rate of introduction of new crop protection compounds over the past 70 years. While numerous studies over the past five decades have signaled the ongoing decline in the numbers of new active ingredients (AIs), a detailed analysis of the trends in the rate of introduction of crop protection compounds shows a more complex pattern in the overall output of new AIs. The recent (post-2000) decline in the numbers of new herbicides is the primary source of the perceived decline in overall numbers. When herbicides are excluded, the output of new fungicides and insecticides has been relatively constant, especially for the past 20 years. A notable observation is that innovation, as measured by the number of compounds representing a new chemical class (First-in-Class) has been relatively constant for the past 70 years, and most recently has been driven by the appearance of new fungicides and insecticides. Thus, the discovery and development of new AIs for crop protection and public health continues, in spite of the many challenges and changes to the Industry. © 2021 Society of Chemical Industry.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".