Special issue: Behavior in networks (II)
Bibliographic record
Abstract
This is the second of two special issues on traveler behavior in networks, following an International Workshop on Behavior in Networks held during July 2004 in Seoul, Korea that acted as a forum for the exchange of ideas, knowledge and experience among overseas and local practitioners and researchers.In the first special issue, six papers have been selected, in which various kinds of traveler behavior in transportation networks have been presented by Lee et al. (2005), Chen and Ji ( 2005), Nam et al. (2005), Lim et al. (2005), Wong et al. (2005) and Asakura and Iryo (2005).Lee et al. (2005) have considered a multistep ahead prediction algorithm of link travel speeds using a Kalman filtering technique in order to calculate a dynamic shortest path.Chen and Ji (2005) have examined three definitions of optimality for finding the optimal path under an uncertain environment.Nam et al. (2005) have examined a mode choice decision by considering not only travel time but also reliability of its modes.Lim et al. (2005) have considered the continuous network design problem under a stochastic environment.Wong et al. (2005) have considered the micro-searching behavior for urban taxi services.Asakura and Iryo (2005) have focused on tracking travelers' data using mobile phone for monitoring and analyzing individual travel behavior.The expansion of transportation networks and the emergence of new technologies have generated an urgent need for advanced models and solution algorithms so as to provide better understanding of the traveler behavior on networks in response to various changes.This special issue on "Behavior in Networks (Ⅱ)" is a collection of another six selected papers presented at the International Workshop on Behavior in Networks held on 22 nd -23 rd July, 2004.The workshop placed greater emphasis on discussions of how new models and advanced methods can be used for
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.095 | 0.027 |
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".