An integrated barn-biofilter-greenhouse system
Bibliographic record
Abstract
A prototype was built to evaluate the performance of an integrated barn-biofilter-greenhouse system.In order to determine the material for solar storage, a preliminary experiment was conducted in which three identical bins (0.024m3) were used to compare the potential of gravel, soil, and woodchips for passively storing energy inside a solar greenhouse.All three materials stored maximum heat at a depth of 76 mm,with gravel storing approximately 7 .25 and 7.73 W more daily average sensible heat energy as compared to soil, and woodchips, respectively.A vertical airflow biofilter (3.34x3.34m)was constructed inside a solar energy greenhouse (floor area of 15 x 6.1 m); exhaust air from a bam was passed through the biofilter for odour treatment before being released into the greenhouse.A booster fan was used to provide a steady airflow rate of 1.4 m3/s to the biof,rlter.Data were collected from October 19 to December 6, 2007 .The maximum temperature drop along the 15.5m length of the insulated (R-20) duct carrying the exhaust air from the bam to the biofilter was 7"C.The lowest temperature recorded on top of the biofilter surface was 1.3oC when the biofilter booster fan was not working, while the lowest floor temperature was -3oC.On the coldest day in December, the daily average temperature inside the greenhouse was 43oC evenwhen the biofilter booster fan was not in service, whereas the outdoor daily average temperature was -25"C.In order to keep the minimum greenhouse temperature at 10oC, the maximum required volumetric flow rate of barn exhaust air at 15oC was 1.60m3/s.Ma*im.,hydrogensulfide (HzS) removal effrciency was 55010.The weekly average concentration of carbon dioxide (COz) inside the greenhouse varied from 841 to 1536 ppm.The system has shown promise for creating an environment suitable for plant growth inside the greenhouse using a waste gas stream from a hog barn to provide both auxiliary heat and enhanced COz levels.I would also like to acknowledge Dr. Gary Crow for his statistical input and patience in answering all of my questions, and Professor Thomas Henley for always being a source of insight, encouragement and perspective.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".