Effects of grain-producing cover crops on rice grain yield in Cabo Delgado, Mozambique
Bibliographic record
Abstract
ABSTRACT Besides providing benefits to the environment such as soil protection, release of nutrients, soil moisture maintenance, and weed control, cover crops can increase food production for grain production. The aim of this study was to evaluate the production of biomass and grain cover crops (and its respective effects on soil chemical and physical attributes), yield components, and grain yield of rice in Mozambique. The study was conducted in two sites located in the province of Cabo Delgado, in Mozambique. The experimental design was a randomized block in a 2 × 6 factorial, with four repetitions. Treatments were carried out in two locations (Cuaia and Nambaua) with six cover crops: Millet (Pennisetum glaucum L.); namarra bean (Lablab purpureus (L.) Sweet), velvet beans (Mucuna pruriens L.), oloco beans (Vigna radiata (L.) R. Wilczek), cowpea (Vigna unguiculata L.), and fallow. Cover crops provided similar changes in chemical and physical properties of the soil. Lablab purpureus, Vigna unguiculata, and Mucuna pruriens produced the highest dry matter biomass. Vigna unguiculada produced the highest amount of grains. Rice grain yields were similar under all cover crops and higher in Cuaia than Nambaua.
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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 | 0.000 |
| 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) | 0.001 | 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".