The Raised Bed System of Cultivation for Irrigated Production Conditions
Bibliographic record
Abstract
The adoption of conservation agriculture technologies, which are characterized by minimal soil disturbance (tillage) before seeding (with the ultimate aim being zero-till seeding) and by diverse strategies to increase crop residue retention on the soil surface to ensure full ground cover (leading essentially to biological tillage) over time, has dramatically increased in many countries over the past 25 years. For example, there are now over 28 million ha of zero-till seeding in Latin America with the bulk concentrated in the southern cone countries of Brazil, Argentina, and Paraguay (Derpsch, 2001). Table 1 lists the adoption of zero-till in the world up to 2001 (Derpsch, 2001). Much of this acreage is zero-till with residue retention. However, upon closer inspection, the adoption of reducedzero-till seeding combined with surface crop residue retention in the countries mentioned above as well as other large area adopters such as the United States, Canada, and Australia, and particularly for wheat production systems, has occurred mainly by large-scale farmers and nearly universally for rainfed production systems with a few exceptions where sprinkle irrigation is used. The apparent exclusion of small-scale farmers in general and for essentially all surface-irrigated production systems (especially where irrigated wheat is a major crop in the system) has several explanations.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.010 |
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".