Thermal Imaging of a Stored Grain Silo to Detect a Hot Spot
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Bibliographic record
Abstract
A hot spot is a localized high temperature zone in a grain bulk and normally spoilage begins in this location. Many sensors need to be installed throughout the bin to detect hot spots by measuring grain temperature. A non-contact method to detect a hot spot in a stored grain silo would be beneficial. The capability of thermal imaging to detect a hot spot in an experimental silo (galvanized steel, 1.5-m diameter and 1.5-m height) filled with barley was studied. An artificial heat source was placed at nine locations inside the grain bulk and set at four temperature levels (30C, 40C, 50C, and 60C) in each location. The outer surface of the silo wall and the top surface of the grain bulk were thermally imaged up to 48 h at each treatment (n = 3). The temperature of the top surface of the grain bulk was significantly (a = 0.05) higher (0.4C to 2.6C) than the atmospheric temperature after 48 h of hot spot establishment. The hot spot was detected from the thermal images of the silo wall and grain bulk (as a high temperature region) when it was located 0.3 m from the silo wall and 0.3 m below the grain surface, respectively. The hot spot was not detected on the thermal images of the silo wall when the wind velocities were 1.0, 1.5 and 2.0 m/s, and immediately after wind (n = 3). Similarly, thermal imaging did not detect the hot spot on the grain bulk when the ambient temperature was 1C (hot spot = 30C), and on silo wall when the ambient temperature was -8C (hot spot = 60C) (n = 3). The surface temperature of the grain bulk decreased with increasing moisture content. It was 25.8C, 24.3C, 23.4C, 22.8C, and 22.4C for the grains with 8%, 12%, 16%, 20%, and 24% moisture content, respectively, when the room temperature was 26C (n = 20). Thermal imaging can not be used as an independent method to monitor the grain temperature in a silo.
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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 | 0.001 |
| 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