Studies on different liquid manure injection tools under laboratory (soil bin) and grassland conditions
Bibliographic record
Abstract
In this study, five different existing liquid manure injection tools (three sweep-types and two disc-types) were evaluated both in the soil bin and at three prairies with heavy clay, coarse sandy loam with stone, and fine sand soil. In the soil bin, the effects of injection depths and tool forward speeds on soil cutting forces and soil disturbance were investigated. While in the field studies, the effects of injection depths and manure application rates on soil disturbances, odor and ammonia concentration, and agronomic response by crop damage and yield were studied. In the soil bin conditions, among the sweeps, sweep A injection tool required the lowest draft force due to its smallest cutting width and rake angle. On the average, sweep B and sweep C required 12 and 97% more draft force than sweep A sweep due to their wider cutting width. In the field study, highest soil disturbance occurred in clay soil due to its wet soil condition. No significant differences in odour concentration were observed between two selected treatments. Similarly, no ammonia concentration was detected from the surface except for higher application rate (112 m 3/ha) combined with shallow injection depth (80 mm) in clay soil. (Abstract shortened by UMI.)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".