Replication of experiments in anaerobic digestion: simpler, greater and easier than you had ever hoped
Bibliographic record
Abstract
Reliability of results in science is done by replication of experiments, but it is often expensive and labour intensive. Indeed, operating anaerobic digesters in a continuous mode requires some experience and a lot of manpower. The number of anaerobic digesters that can be handled in parallel is therefore limited. In practice, experiments are often repeated several times, starting always with a fresh inoculum. One approach is to store and to restore the activity of the same inoculum, with no guarantee on the preservation of the functioning. Unfortunately, there is no consensus on any standardized strategy for the long-term preservation of complex ecosystems. Here, we present a multiplexed chemostat system that minimizes costs and manpower for operating several anaerobic digesters in parallel, and present a first application of the system. Up to thirty reactors may be operated by a single person. This system may unlock ecological studies as well as process optimization in the context of anaerobic digestion, where several conditions need to be tested in parallel. The vocation of this device is to make it available to the scientific community.
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 | 0.041 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.009 |
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".