Bibliographic record
Abstract
Fermentative hydrogen production from biomass using mixed anaerobic cultures has a greater potential to be developed as a practical biohydrogen system than systems utilizing pure cultures. To optimize hydrogen production, it is important to inhibit hydrogen consumers during glucose fermentation. Long chain fatty acids (LCFAs) are inhibitors of aceticlastic methanogenic bacteria and these fatty acids could act as hydrogenotroph methanogenic inhibitor in fermentative hydrogen production. Batch studies were conducted to assess the effects of two C18 LCFAs on microbial hydrogen production from glucose under mesophilic conditions. Experiments were conducted using different concentrations of linoleic acid (LA) and oleic acid (OA) at 37+/-1°C. The effects of initial pH in the presence of two C18 LCFAs on hydrogen production were assessed by controlling the initial pH. Glucose was re-injected on day 4 or day 5 to examine the combined effect of LCFA and volatile fatty acids (VFAs) on hydrogen production and also the inhibition time dependence. (Abstract shortened by UMI.)Dept. of Civil and Environmental Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .C46. Source: Masters Abstracts International, Volume: 44-03, page: 1471. Thesis (M.A.Sc.)--University of Windsor (Canada), 2005.
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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".