Thermal Performance and Degradation of Urea as a Result of Time and Storage Location
Bibliographic record
Abstract
This research aims to analyze the degradation of urea in its storage process, observing the environmental conditions of the storage building and also the storage time. In order to carry out such analysis, a field experiment with a completely randomized design (CRD), 3 × 4, was set up, with eight 50 kg bags divided in four different locations, being a shed, an underground basement, and two reduced models built in wood, where one received thermal input minimization treatments and the other with simplified coverage and closings. Within the four environments, temperature (DBT) and temperature (WBT) were measured, where it was possible to obtain the relative humidity values enabling thus crossing the internal climatic data with the process of urea degradation in storage for a period of three months. To analyze urea degradation, the analytical manual of fertilizers of the Ministry of Agriculture (MAPA) was used. The analyses of the physicochemical parameters of nitrogen content, density, humidity, total bag weight, and also granulometry were carried out monthly. The data were validated by one (ANOVA) and Tukey test with 5% probability of error. The results show that the environments with higher temperatures had a higher volatilization of nitrogen, the losses reached about 13% during the evaluated period. Losses of up to 5.3% of the density of the analyzed samples were also observed. In relation to the initial weight of the bags, losses totaled a reduction of 1.5%. It was also possible to conclude that the lowest losses were recorded in places where there was greater ventilation during the day, where the thermal inertia of the building dissipated.
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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 | 0.001 |
| 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) | 0.001 | 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 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".