Bibliographic record
Abstract
Urban waste generation and disposal remains a major global issue. As the world’s population grows past the 7 billion mark and more people move to urban areas, the amount of waste generated will grow accordingly. The most promising solutions to this problem are waste to energy technologies in the form of biological treatment of organics through anaerobic digestion and thermal decomposition via plasma arc gasification. These two technologies can be used in the urban environment separately or complimentarily to reduce the volume of the waste being processed while also generating heat and power (CHP) and reducing transportation costs and greenhouse gas emissions. In this research, the feasibility of heating a small-scale anaerobic digester using an air source heat pump and solar heat gains from a greenhouse located on the roof of an urban building in Montreal, Canada, is investigated during the coldest month of the year. Small-scale implementation of anaerobic digestion systems for backup and emergency power is also investigated for both the urban and rural environments as a solution for increased grid blackouts caused by more frequent and more severe storms. Derating curves are determined for generators operating under extreme unbalanced load conditions in both the urban and rural environments. The benefits and disadvantages of induction and synchronous generator systems are presented for small to large-scale systems (20kW to 2000kW).
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".