Bibliographic record
Abstract
The mainstay method of equilibrium assignment methods is based on adaptation of the linear approximation algorithm. Practically all commercial software packages for transportation planning offer a version of this algorithm. In the early days of personal computing, when random-access memory (RAM) was limited, this method was the most appropriate one to use because it requires little intermediate storage. As personal computers became more powerful and RAM became plentiful, the drawbacks of the linear approximation method became evident to practitioners. A measure of convergence is the relative gap, which measures the relative difference between total travel time and total travel time on the shortest paths. Relative gaps of less than 10 –4 are difficult to reach with this method. Alternative assignment methods, based on algorithms that have better convergence rates, are known and can obtain finer solutions. Changing to new algorithms would appear to be a trivial task; however, it is not the case. The issues related to changing assignment algorithms pertain to the uniqueness of equilibrium paths, flows, and times. Examples of the expected changes in results for both standard multiclass assignments and for one complex model, which is equilibrated with feedback procedures, are presented. An adaptation of the projected gradient with path flows is used to represent the modern algorithms, which can reach relative gaps of 10 –6 or better. Differences in relevant results are relatively small. Nevertheless, practitioners are careful to reproduce results and may face a challenge to accept slightly different results with a faster converging algorithm.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.030 | 0.010 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".