Evaluation methods on manure exposure from liquid manure injection tools
Bibliographic record
Abstract
Rahman, S., Chen, Y., Zhang, Q. and Lobb, D. 2005. Evaluation methods on manure exposure from liquid manure injection tools. Canadian Biosystems Engineering/Le genie des biosystemes au Canada 47: 6.9 6.16. Laboratory and field studies were conducted to explore evaluation methods on manure exposure (refer as to manure being not covered by soil, but exposed to the air) for liquid manure injection tools. The laboratory study was conducted in an indoor soil bin with three sweeps (small, medium, and large) at three injection depths (50, 100, and 150 mm), two tool forward speeds (0.6 and 1.4 m/s), and two soil moisture contents (14 and 18%). Soil surface profiles measured with a laser profiling system were used to define two parameters, risk and beneficial factors, as well as manure exposure and soil cover indices, to assess the risk for manure exposure following liquid manure injection. These parameters indicated that a larger sweep operating at greater injection depth and lower forward speed resulted in low risk for manure exposure on the soil surface. Soil moisture content did not significantly affect the manure exposure. The field study was conducted with a commercial injector consisting of 13 sweep injection tools in a clay soil at three manure application rates (28, 56, and 112 m/ha) and an injection depth of 100 mm. Following the manure injection, line-transect and image analysis methods were used to quantify the percentage of the surface area covered with manure (manure cover), and the odour concentration and emission rate were determined by a wind tunnel and a dynamic dilution olfactometer. The results showed that manure cover increased at an increased manure application rate. No statistically significant effect of manure application rate on odour concentration was observed, and the odour data were not correlated to the manure cover data.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".