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
The smart city is a concept of utilizing digital technologies to improve and enhance the lives of a city's inhabitants. This concept has been the subject of increasing interest over the past few years. However, most studies address improving aspects of a city's infrastructure, such as information security, privacy, communication networks, government, and transportation. Noticeably absent from the subject matter of these studies are social problems, such as poverty and homelessness. In this paper, we explore how technology can be harnessed to mitigate homelessness. We introduce eight novel heuristic algorithms that create a desirable homeless-to-housing assignment with regards to homeless individuals' characteristics and the nature of services. We discuss the efficiency of each of the algorithms through simulations. Our best performing algorithm obtains 92% accuracy in comparison to the optimal solution and 99.7% fairness. The algorithms are compared in terms of execution time, solution accuracy, fairness, and the relative difference with the optimal solution of this NP-hard problem.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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