Development of a Livestock Odor Dispersion Model: Part I. Model Theory and Development
Bibliographic record
Abstract
ABSTRACT A livestock odor dispersion model (LODM) was developed to predict mean odor concentration, odor frequency, instantaneous odor concentration, and peak odor concentration from livestock operations. This model is based on the Gaussian fluctuating plume model and has the ability to consider the instantaneous concentration fluctuations and the differences between odor and traditional air pollutants. It can predict odor frequency from the routine hourly meteorological data input and deal with different types of sources and multiple sources. Also, the relationship between odor intensity and odor concentration was incorporated into the model. IMPLICATION The LODM is a model developed especially for odor from livestock buildings and manure storage. It has the ability to consider short time odor fluctuations, which is important from research and management perspectives. The weighted odor-exceeding half-width method used in LODM to predict odor frequency is an improvement over past studies. The model can accept routine hourly meteorological input and deal with different and multiple sources. It can also consider the relationship between odor concentrations and intensity. These characteristics of LODM make it especially relevant for odor research and application.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".