Multicriteria evaluation of direct seeding mulch based cropping systems (DMC) in the context of small scale farmers in the Cerrados Region of Brazil
Bibliographic record
Abstract
In central Brazil, the tropical savannah ecosystem known as Cerrado, small scale farmers (around 20 ha) occupy marginal land scattered among large commercial farms on wide plateaux. Close to 90 % of the agricultural land farmed by large holders in the Cerrado is managed under direct seeding mulch based cropping systems (DMC). However, this system is almost not used by small scale farmers of the same region, even if their soils are often very susceptible to degradation (erosion, losses of soil organic matter), and they crucially need to stabilize their corn production in other to insure sustainability. Since 2005, Embrapa and Cirad tried to develop, in interaction with small farmers of Unaí region (Minas Gerais), new DMC systems adapted to their conditions and compatible with their own objectives. However, even if those systems increase soil fertility significantly, they also often deeply modify crop management as well as the use of resources available at farm level. Such systems have thus strong consequences on economical, environmental or social aspects of the system, (Scopel et al., 2005).
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".