Prediction of woodchip-induced lateral pressures on biofilter walls
Bibliographic record
Abstract
A biofilter containing structure is susceptible to structural failure just like other agricultural storage structures.A major factor that causes structural failure of most bins is the lateral pressure exerted by stored material on the bin wall.Four experimental studies were conducted to evaluate lateral pressures in a biofilter bin using woodchips as medium material.The objective of the first study was to determine the physical properties of media material, which are necessary for calculating wall loads.Porosities, bulk densities, angles of repose, and coefficients of friction of 100:0, 80:20, and 60:40 woodchip:compost mixtures at moisture contents of 40, 60, and 800/o were measured.Porosity decreased, but bulk density, angle of repose, and coeffrcient of friction all increased with increasing moisture content of the media.The second study measured the magnitude of lateral pressure caused by woodchips of different moisture contents (37,45,58, and 60% w.b.) in model bins.Three model biofilter bins were employed.Each bin was 0.5 m by 0.5 m, and 1.2 m fall.Lateral pressures were measured with pressure sensors mounted on the bin wall at 0.2,0.5,0.7, and 0.9 m above the bin floor.Lateral pressures increased as the moisture content of woodchips increased.Existing pressure equations did not accurately predict pressures in biofilter bins in most cases.Biofilters are subject to continuous variation in moisture content because of repetitive wetting and drying phenomenon of the media materials.The third study investigated lateral pressure variation in biofilters due to wetting and drying cycles.The same model amount of support and excellent guidance I received from him throughout my study.I would like to thank him also for being very encouraging to me and for granting me the opportunity to diversify my student experience at the university.
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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 | 0.001 | 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 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".