Sensitivity analysis of alternative model structures for an indicator of ammonia emissions from agriculture
Bibliographic record
Abstract
Ammonia (NH3) emission from agriculture is an environmental and health concern in many nations, and has trans-border impacts. Direct toxicity, terrestrial eutrophication and production of inhalable aerosols (< 2.5 µm diameter) are the specific concerns. Canada, among other northern hemisphere nations, has computed a national inventory of NH3 emissions, and a new emission inventory estimate is being prepared jointly under the National Agri-Environmental Health Analysis and Reporting Program (NAHARP) and National Agri-Environmental Standards Initiative (NAESI). However, there has been a rapid evolution in the models used, and a concomitant change in the NH3-specific data required. This paper compares several model structures and options using Monte Carlo simulation and sensitivity analysis methods. The results indicate the more recent models, that compute a mass balance of NH3 from excretion to landspreading, have tended to focus uncertainty onto the dietary efficiency of animal N nutrition. After excretion, the total ammoniacal nitrogen (TAN) in the manure contributes to NH3 emissions at each stage as the manure passes from the animal housing to storage and to landspreading. There are many variants of these processes because every farm is different, resulting in diminished sensitivity to any one NH3-loss mechanism after excretion. This finding suggests that although NH3-emission factors, the empirical data at the core of the models, are not well characterised (especially for Canadian conditions), it is at least as important to expend research effort on factors that influence TAN excretion. Results of this paper will also guide development of the NAHARP/NAESI model. Key words: NAHARP, livestock, manure, Fraser Valley, ammonium sulphate, PM2.5
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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".