Bibliographic record
Abstract
Glyphosate has become the most widely used herbicide worldwide since 1974 with a global use of 8.6 billion kg (glyphosate active ingredient) between 1974 and 2014. This study reports on glyphosate resistant (GR) weeds and their resistance mechanisms based on global scientifically reported cases. Forty-nine different weed species have evolved resistance to glyphosate in 29 countries with a total of 318 identified cases worldwide. Fifty percent of these resistance cases were found in glyphosate-resistant cropping systems. There were 255 identified cases (80.2%) of glyphosate resistance in the top five countries (in terms of number of cases and species), namely USA, Australia, Argentina, Brazil, and Canada. The five most popular weed species (in terms of number of cases) found to be resistant to glyphosate were Conyza canadensis, Amaranthus palmeri, Amaranthus tuberculatus, Lolium perenne ssp. Multiflorum,and Ambrosia artemisiifolia with 42, 42, 29, 26, and 21 reported cases, respectively. Out of 49 weed species, 19 GR weed species were found to not only be resistant to glyphosate but also to other herbicide sites of action (multiple herbicide resistance). Glyphosate resistance mechanisms in weeds include (1) target-site alterations: target-site mutation and target-site gene amplification; and (2) non-target-site mechanisms involving different modes of exclusion from the target site: reduced glyphosate uptake, reduced glyphosate translocation, and enhanced glyphosate metabolism. It is essential to have an integrated weed management program that includes not only smart herbicide mixtures and rotations, but also cultural, manual, mechanical, and crop-based weed management methods.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".