Structuring of a Haplortox by Cover Crops and Their Effects on the Yield of Soybean Grains
Bibliographic record
Abstract
The intense agricultural machinery traffic over the plantation ground can lead the erosion and growth difficulty. The goal of this study was to evaluate the soya bean yield after the implantation of species named “recoverable”, of soil structure. The experiment was developed in plots of 20 m × 25 m, located in the Agronomic Institute of Parana (IAPAR), in Santa Tereza do Oeste, Paraná. The plots were cultivated by direct sowing of the following species, considered as treatments: sunn hemp (Crotalaria juncea), rattlebox (Crotalaria spectabilis), velvet bean (Mucuna aterrima), pearl millet (Pennisetum glaucum), pigeon pea (Cajanus cajan), dwarf pigeon pea (Cajanus cajan) beside them no-tillage and no-cover crop planting traditional system (control). Soil samples were collected from 0-10 cm, 10-20 cm, and 20-30 cm-layers with 4 repetitions on each treatment. Soil density and porous soil space were also determined. The plot yield of soybean grains was evaluated over an area of 4.5 m2 for each treatment and grain moisture corrected to 13%. The treatments’ mean yields were compared using the Tukey test at 5% probability. The dwarf pigeon pea and the rattlebox were the most efficient cover crops in the reduction of soil bulk density in 0-10 and 10-20 cm depths. The soybean grain yield did not differ between the evaluated treatments, possibly due to the good precipitation conditions during the soybean growing cycle.
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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".