Development of a simple and affordable method of measuring ammonia volatilization from land applied manures
Bibliographic record
Abstract
Quantifying ammonia (NH3) flux following fertilizer and manure nitrogen (N) application is crucial to develop sound management practices. Traditional methods used for obtaining these measures are expensive, inefficient, or inaccurate. The objective of this study is to develop a method using a passive dosimeter and a semi-open static chamber to provide an economical and simple solution to measure NH3 loss following nitrogen application. Dosimeter tubes were commercially developed to measure ammonia exposure, providing a time-weighted average. In this study, chicken manure was applied to short grass and the ppm h reading obtained using the dositube ammonia method was calibrated against a reference measure of NH3 loss (kg N ha−1) using a wind tunnel and acid trap method. A calibration was developed (Estimated Total Loss (kg N ha−1) = (0.217Dw) − (0.034D) + 0.71), which requires the dositube (D, ppm h) to be read every 24 h and placed at a height of 0.15 m in the dositube chamber, with wind speed (w, m s−1) measured at a height of 0.3 m and averaged over the coinciding time period. This calibration may also be applied where dositubes are read every 48 h; however, 24 h periods are recommended to achieve the greatest accuracy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".