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
Researchers widely use the two-fluid model (TFM) to evaluate the performance of urban networks. However, the TFM is deterministic and does not capture the stochastic relation between speed and density. The present study develops a modified two-fluid model (MTFM). The variance function or the distribution of speed or travel time for a given density is incorporated using a percentile-based indicator, travel time uncertainty (TTU). The percentile-based indicators for the speed distribution are more robust than the variance or other moment-based indicators. The effect of TTU is incorporated using two parameters, α, and β. The applicability of the proposed MTFM is demonstrated using empirical data collected at the corridor and network levels. The TFM and MTFM were calibrated by formulating a nonlinear optimization problem. Based on the investigation using the corridor and network-level data, it was concluded that the MTFM showed better performance than the existing 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 | 0.000 | 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