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
Human mobility is a multidisciplinary field of physics and computer science and has drawn a lot of attentions in recent years. Some representative models and prediction approaches have been proposed for modeling and predicting human mobility. However, multi-source heterogeneous data from handheld terminals, GPS, and social media, provides a new driving force for exploring urban human mobility patterns from a quantitative and microscopic perspective. The studies of human mobility modeling and prediction play a vital role in a series of applications such as urban planning, epidemic control, location-based services, and intelligent transportation management. In this survey, we review human mobility models based on a human-centric angle in a datadriven context. Specifically, we characterize human mobility patterns from individual, collective, and hybrid levels. Meanwhile, we survey human mobility prediction methods from four aspects and then describe recent development respectively. Finally, we discuss some open issues that provide a helpful reference for researchers' future direction. This review not only lays a solid foundation for beginners who want to acquire a quick understanding of human mobility but also provides helpful information for researchers on how to develop a unified human mobility model.
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 | 0.001 | 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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