Low-Temperature Anaerobic Digestion of Agricultural Organic Wastes: Performance Evaluation, Influencing Factors, and Nutrient Recovery for a Circular Economy
Bibliographic record
Abstract
Intensification of agricultural activities leads to a greater waste generation. Efficiently dealing with organic waste can benefit the environment and be lucrative for all. Organic matter left on land emits greenhouse gases that accelerate the global warming. These emissions can be captured and converted to biogas using anaerobic digestion (AD) biotechnology. Moving towards a circular economy, different techniques can be combined to improve sustainability. This research work investigates the potential of AD to treat different kinds of agricultural wastes at a low temperature, using adapted inoculum and different modes of operation. Starting from inoculum preparation, this thesis covers the details about the AD of mixed agri-wastes like manure and crop residues, biogas production, nutrient recovery and circular economy. A thorough review of the integration of a nutrient recovery option, microalgae cultivation, with AD is conducted. A corresponding outlook for Canada considering regional weather and agricultural conditions is provided. A preliminary study is also carried out on microalgae cultivation using a digestate created post-AD treatment.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".