Special issue on “Transportation and Logistics with Autonomous Technologies”
Bibliographic record
Abstract
The development of autonomous technologies, such as self-driving cars and unmanned vessels, is progressing at a fast pace. While design and safety concerns have been the focus of debate around these new technologies, recent research is also concerned about their implications in transport operations and logistics. This special issue welcomes articles on transportation and logistics with autonomous technologies, covering a broad range of aspects such as transportation of goods and passengers, distribution networks, material handling, and warehousing. The call is open to autonomous technologies in a broad sense, including fully autonomous and partially autonomous systems. Thus, for example, operations with unmanned vehicles remotely controlled, management of fleets mixing both autonomous and non-autonomous vehicles, and coordination between robots and human workers in warehouses are also relevant. Contributions on both methodology and practice of operations research in connection with transportation and logistics with autonomous technologies are equally welcomed, including analytical methods and models for decision-making problems, and their application. The deadline for submissions is October 31, 2019. Papers will be peer-reviewed according to the editorial policy of the International Transactions in Operational Research (ITOR), published by the International Federation of Operational Research Societies (IFORS). They should be original, unpublished, and not currently under consideration for publication elsewhere. Contributions should be prepared according to the instructions to authors that can be found on the journal homepage. Authors should upload their contributions using the submission site http://mc.manuscriptcentral.com/itor, indicating in their cover letter that the paper is intended for this special issue. Other inquiries should be sent directly to the Guest Editors in charge of this issue: Mario Guajardo (mario.guajardo@nhh.no), Teodor G. Crainic (teodorgabriel.crainic@cirrelt.net), Debjit Roy (debjit@iima.ac.in), Stein W. Wallace (stein.wallace@nhh.no).
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.195 | 0.099 |
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".