In-storage psychrophilic anaerobic digestion: acclimated microbial kinetics
Bibliographic record
Abstract
In-storage psychrophilic anaerobic digestion develops by microbial acclimation in covered swine-manure storage tanks, producing CH4 and stabilizing organic matter. To optimize the system's performance, the process kinetics must be understood. The objective of this study was to evaluate kinetic parameters describing the major stages in the digestion process, and to investigate the effect of temperature acclimation on these parameters. Specific activity tests were performed using manure inocula and five substrates at three incubation temperatures. Extant substrate activities were determined analytically for each case, and intrinsic kinetic parameters for glucose uptake were estimated by grid search fitting to the Monod model. The results demonstrate that this acclimated microbial community exhibits different kinetic parameters to those of the mesophilic communities currently modelled in the literature, with increased activity at low temperatures, varying with substrate and temperature. For glucose, the higher uptake is accompanied by lower microbial yield and half-saturation constant. Decomposing these values suggests that active psychrophilic and mesophilic microbial populations co-exist within the community. This work also confirms that a new method of assessing microbial substrate kinetics must be developed for manure microbial communities, separating microbial mass from other suspended organics.
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 | 0.001 |
| 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".