Development of wireless monitoring and automated aeration control system for sugar beet conditioning in outdoor piles
Bibliographic record
Abstract
Abstract Harvested sugar beets (Beta vulgaris L.) are stored in outdoor piles exposed to ambient weather conditions and fluctuating temperatures during the winter storage period, lasting six months. About 10% of the sugar beet piles in Alberta are aerated. Aeration is primarily conducted to reduce temperature and slow down sugar loss. As a standard protocol, the piles are aerated with one temperature sensor for every 5 fan lines, each fan line covering approximately 1000 tonnes of sugar beets. This results in hot spots not being detected at locations away from the sensor, thus affecting fan operation at critical times. Therefore, this study was conducted to design and install 16 custom-designed temperature sensor cables with 64 temperature sensors for 4 consecutive fan lines at Taber sugar beet piles. This research was executed to quantify the benefits of installing four sensor cables for each fan line. Each sensor cable had temperature sensors at 4 different depths separated by 60 cm. The sensor cables were connected to a multichannel node and then to a cloud network for remote monitoring. The interstitial temperature from the sensors and ambient weather data from the weather station were transmitted to the automated fan control system. Based on the complete temperature profile obtained from the multiple sensor cables installed in each fan line, the cloud-based fan control system operates the fans only when required. Furthermore, multiple hot spots were identified with the new sensor system. whereas the single sensor didn‘t detect multiple hot spots, extensive monitoring of piles by climbing to the top of the pile can be avoided by multiple sensors and remote monitoring.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".