Effect of aluminide coatings and wheat handling parameters on erosion in wheat storage bins.
Bibliographic record
Abstract
Recent studies demonstrate that aluminide coatings are beneficial to wear and erosion resistance and also that oxygen-active elements such as yttrium can remarkably improve this resistance. In this research, the micro-hardness of the aluminide coatings with and without yttrium on 1045 steel was investigated using a micro-mechanical probe. Wear of the samples was measured using a pin-on-disk tribometer. The erosion loss of specimens against soft and hard wheat was also evaluated using a slurry erosion test machine. The data was analyzed statistically using a 3-factor completely randomized design to study the effect of wheat varieties (soft and hard wheat), moisture content at three levels (10±0.5, 15±1.5, and 20±2 % (wet basis)), and rotary velocity of the slurry erosion machine at three levels (200, 400, and 600 rpm) on erosion resistance. The results showed that the aluminide coatings improved the wear and erosion resistance of substrate steel 1045; yt-trium markedly improved the hardness of the aluminide coating and its wear and erosion resistance. The erosion loss of materials was significantly (p<0.01) influenced by the type of wheat, moisture content and rotary velocity. Both aluminide coatings showed higher wear and erosion resistance than 1045 steel substrate.
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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 | 0.001 | 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".