Sensitivity analysis of a livestock odour dispersion model (LODM) to input parameters: Part I, source parameters and surface parameters
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Sensitivity analysis was conducted to evaluate a livestock odour dispersion model (LODM) with respect to its input source parameters including: stack height, stack diameter, exit velocity, exit temperature, and emission rate and surface parameters (surface roughness, albedo, and Bowen ratio). An elasticity value was calculated together with the average change of odour concentration and frequency to indicate the model sensitivity to its input parameters. Results showed that the source parameters have the similar medium impact on model predicted hourly odour concentrations and hourly odour frequencies. The sensitivity of emission rate is lower to odour frequencies than odour concentrations. Among the three surface parameters, LODM has low sensitive to surface roughness while its sensitivity to albedo and Bowen ratio is negligible. In practice, in order to reduce the odour impact from livestock operation, the most effective way is to reduce the emission rate.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 it