Tillage systems and nutrient sources affecting soil cover, temperature and moisture in a clayey oxisol under corn
Bibliographic record
Abstract
Tillage affects soil physical properties, e.g., porosity, and leads to different amounts of mulch on the soil surface. Consequently, tillage is related to the soil temperature and moisture regime. Soil cover, temperature and moisture were measured under corn (Zea mays) in the tenth year of five tillage systems (NT = no-tillage; CP = chisel plow and single secondary disking; CT = primary and double secondary disking; CTb = CT with crop residues burned; and CTr = CT with crop residues removed). The tillage systems were combined with five nutrient sources (C = control; MF = mineral fertilizer; PL = poultry litter; CS = cattle slurry; and SS = swine slurry). Soil cover after sowing was greatest in NT (88 %), medium in CP (38 %) and lowest in CT treatments (< 10 %), but differences decreased after corn emergence. Soil temperature was related with soil cover, and significant differences among tillage were observed at the beginning of the growing season and at corn maturity. Differences in soil temperature and moisture in the surface layer of the tilled treatments were greater during the corn cycle than in untilled treatments, due to differences in intensity of soil mobilization and mulch remaining after soil management. Nutrient sources affected soil temperature and moisture in the most intense part of the corn growth period, and were related to the variation of the corn leaf area index among treatments
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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) | 0.000 | 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 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".