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
Strip tillage is a conservation tillage practice that isolates tillage to a narrow strip of soil where individual rows will be planted for the upcoming crop. Strip tillage was developed in the 1970s and most strip tillage machines from that period incorporated the use of rotary hoes for seedbed preparation. In recent years, strip tillage machines have been largely re-designed using a system of shanks and coulters which provide greater fuel efficiency and faster operating speeds than previous designs. New commercial strip tillers have become widely available in the United States and parts of Canada and the practice of strip tillage is being increasingly implemented. Strip tillage is optimal in areas that are prone to soil erosion and drought, have compacted soils or plough pans, or for small-seeded crops requiring a cultivated seedbed. Advantages of strip tillage for sugarbeet (Beta vulgaris L.) production include reduced soil erosion, enhanced moisture retention relative to full-width conventional tillage, improved seedbed environment relative to direct drilling, optimum fertilizer placement, increased carbon sequestration, and reduced fuel consumption. Challenges related to strip tillage systems for non-GMO sugarbeet production include weed control and management of cold, wet soils. Results of most U.S. research studies show that strip tillage has not differed from conventional tillage systems for sugarbeet yield and sugar production. Strip tillage for sugarbeet production was superior to direct drilling in most cases. Given the similar yields and potential cost savings from fuel and labor, strip-tillage is a feasible and potentially profitable alternative to conventional full-width tillage for sugarbeet production.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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