Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Glyphosate-resistant (GR) sugar beet became commercially available to US sugar beet growers in 2008 and was rapidly adopted. Prior to the availability of GR sugar beet, growers would commonly make 3-5 herbicide applications. This often resulted in some crop injury, but was accepted to reduce the impact of weeds. In addition, non-GR sugar beet was cultivated 1-3 times and often followed by hand weeding. The introduction of GR sugar beet drastically reduced the complexity of weed management. Concerns about GR weeds in the United States also apply to sugar beet growers. Changes in weed management strategies will be required to keep this technology. Sugar beet is arguably one of the most suitable crops for GR technology because: (1) none of the herbicides registered for use in this crop was very effective without risking crop injury; (2) sugar beet cannot be grown in the same field year after year owing to disease concerns and thus requires a 3-4 year rotation; (3) pollen-mediated gene flow is negligible from the sugar beet crop because it is a biennial and harvested before it flowers; (4) the processing of harvested roots to extract the sucrose rapidly degrades the DNA in the extracted raw juice and subsequent refining so that no DNA is present in the finished sugar; (5) studies have shown that processed GR beet sugar is identical to non-GR beet sugar, as well as cane sugar. © 2016 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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 it