Dataset of thermal behaviour and weather data of thermal disinfestation of Sitophilus oryzae in plastic bags using solar heating.
Bibliographic record
Abstract
These datasets are related to solar disinfestation of wheat using polyethylene bags and direct solar radiation to control Sitophilus oryzae. The data collected in Canada and Egypt. In Canada, Clear polyethylene bags of wheat were used for thermal control using solar radiation. There were four treatments of different wheat amount, 16, 21, and 25 inside clear bags in wood boxes and another 21 kg of wheat in plastic bag not in a wood box. The solar heating for all treatments were investigated in the field under two different conditions. First, the temperature profile inside the bags was recorded every morning, and the grains were mixed and stacked in foam boxes during the night over five days. Second, the temperature profile was recorded continuously during day and night over six days. Different weather condition, ambient temperature, solar radiation, wind direction, and wind speed were collected during both experiments using a weather station located on the filed. In Egypt, clear plastic bags and woven plastic bags with 16 kg of wheat were used for solar heating over 5 d. Generally, the temperature profile data inside the plastic polyethene bags under different storage conditions, grain amounts, bags materials and different weather conditions allow other researchers to develop different models to understand the thermal behavior for thermal disinfestation. Solar heating is a very promising disinfestation technique that was successfully used for museum pest control, postharvest pest control, and soil disinfestation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".