Decentralized Urban Composting
Bibliographic record
Abstract
The NYC Compost Project Hosted by Big Reuse is a program funded by the New York City Department of Sanitation (DSNY), which features a community composting facility, located under the Queensboro Bridge in Long Island City, New York. The compost site uses a SG mini system with GORE Covers, which was developed by sustainable generation, and consists of a GORE Cover placed over an aerated static pile. SCS Engineers (SCS) was hired to assist the NYC Compost Project Hosted by Big Reuse with the design and construction of their new site in 2017, which will increase the processing capacity to a maximum of 1,000 cubic yards of food scraps annually. The NYC Compost Project works to rebuild New York City’s soils by providing New Yorkers with the knowledge, skills, and opportunities they need to produce and use compost locally. The compost facility is part of a community-scale composting network. The finished compost is used in community gardens, street trees, and other public greening projects. The following design and operations features are presented in this paper: new site design and layout; feedstock mixing; covered ASP system; turned windrows and screening; monitoring; working surfaces; grading and stormwater management; contact water management; and electric utility service.
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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.037 | 0.007 |
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".