Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper builds upon an idea where a computer can independently detect and segregate garbage without any form of human intervention. This classification is based purely on the material of the item, and is independent of its shape and size. Through our project, we have attempted to introduce an automated waste segregation mechanism - controlled by modules written using Raspberry Pi - that could serve as an alternative to the laborious methods employed currently. The system focuses on the identification of waste that is commonly dumped on the streets, and attempts to segregate items into 12 distinct categories. At the same time, it is also cost-effective and requires minimal maintenance. Following classification; all biodegradable products can be utilized for making compost, and the rest can be recycled. The proposed system can be installed along the streets, and will prove to be beneficial in segregating waste at the site of disposal itself. It can also enable the adoption of an automated waste segregation approach at the municipal level; while ensuring that the process is faster, cleaner and more environment-friendly. Devising such a segregation system will definitely improve the waste management process in India.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it