Computational Analysis to Capture Cluster Lifetime Dynamics
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
Spatial-temporal data analysis aims at uncovering useful insights and patterns from data that is associated with a location and that changes with time. Spatial-temporal data can comprise massive datasets obtained from multiple sources, including mobile devices, cameras, radar, and other types of sensors. Traditional analysis techniques allow researchers to perform several tasks, including classification, regression, and clustering. Specifically, clustering methods have been widely adopted in domains such as transportation, smart cities, and astronomy. However, current clustering techniques fail to analyze a moving cluster from its start to finish, limiting themselves to investigating static clusters. This study introduces a framework that takes into consideration the entire life of a mobile cluster and describes its lifetime based on dynamic spatial-temporal relationships that the cluster has with other clusters or trajectories. The framework is evaluated using two case studies, which involve taxi trajectories and human mobility.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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