Experimental Study of Grain Dryer Noise Emissions
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
There is increasing interest in the environmental noise emissions from grain dryers and the potential impact of practical noise pollution mitigations such as barriers adjacent to dryers. Grain dryers are an essential part of grain production in many parts of the world, including Ontario, Canada. Most dryers are large, stationary units that include a burner to provide process heat and a fan or blower to move heated air through the grain being dried. This study measured sound levels at a range of distances from multiple grain drying facilities in Ontario, Canada, over two drying seasons. It was found that the sound level at a given distance varied substantially, depending on the dryer type and presence of blocking features such as grain bins or buildings. Noise emissions did not necessarily correlate to the size or drying capacity of the facility, with some smaller top dry dryers having higher noise emissions than other much larger tower dryers. Targeted investigations of the impact of practical remediations in the form of physical sound barriers showed sound level reductions were possible that were similar in magnitude to those achieved by highway sound walls along roadways, with most sound reduction being at higher frequencies.
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 | 0.000 |
| 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) | 0.002 | 0.002 |
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