Improved acid trap methodology for determining ammonia volatilization in wind tunnel experiments
Bibliographic record
Abstract
Nitrogen loss through ammonia volatilization is an environmental and economic concern. When acid traps are used with wind tunnels to measure ammonia volatilization, loss of solution volume is observed. As the loss mechanism affects volatilization estimates, a field study was conducted to determine if solution loss from acid traps was due to either selective loss of water through evaporation, loss of bulk solution, or a combination. Two methods for calculating air flow volume through the acid traps were also examined. Solution losses from acid traps averaged 40 mL d−1 (±9.2 mL) from an initial 100 mL, and ammonium concentration increased in close accordance with the dilution–concentration relationship for aqueous solutions. Hence, solution loss was due to evaporation, with virtually no ammonium loss, confirming that the flux calculations using corrected acid trap volumes are required. Failure to correct for the reduced volumes resulted in 9%–224% overestimation of ammonium concentrations. Air flow volumes through acid traps were underestimated by 18.5% when initial and final air flow rates were used compared with continuous cumulative flow measurements. Using cumulative flows and accounting for evaporation loss from acid traps help ensure that treatment differences are not masked by the inherent variability in field-based measurements.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".