Bibliographic record
Abstract
In this thesis, a study was undertaken on the premise that the world population living in urban centers is expected to increase from 3.8 billion to 5.2 billion, from 2005 to 2025, representing 54% and 65% of total world population, respectively. The urban population (UP) growth will produce remarkable amounts of urban food waste (UFW) that will add more pressure on already overloaded municipal solid waste (MSW) management systems of cities. This problem is more serious in countries experiencing major economic growth such as China where UP is expected to increase from 44% to 66% of the total country population, from 1995 to 2025. Asia produces the largest amount of UFW, which is expected to increase from 251 million ton to 418 million ton (45% to 53% of total world UFW) from 1995 to 2025. On site treatment of UFW along with a limited movement of world population from rural to urban areas are suggested to reduce pressure on MS W management system for the upcoming decades. In this thesis, a project was also undertaken to develop compost recipes for urban center such as downtown Montreal. Monthly (June to August) average residential FW production was found to 0.61 (+/-0.13) kg capita-1 day-1 and that of a restaurant was found to be 0.56 (+/-0.23) kg customer-1 day-1. From trial tests, the best compost recipes mixed 8.9 kg, 8.6 kg and 7.8 kg of UFW for every kg of wheat straw, hay and wood shaving, on a wet mass basis. However, quantity and characteristics of FW vary from one month to another; therefore, regular adjustment of compost recipe is recommended. When using wood shavings as bulking agent, it is strongly recommended to correct the acid pH.
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 | 0.001 | 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 | 0.000 |
| 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".