Data standards of use in vehicle and freight tracking via road, ship & rail
Bibliographic record
Abstract
NRC’s AI for Logistics Program (AI4L) is focussed on supporting the next-generation of AI-enabled technologies to improve freight transportation. In addition to direct research interactions, AI4L undertakes activities to support ecosystem development and encourage interest in relevant topics. One of the key challenges to the development of AI-enabled technologies to improve logistics efficiency, fluidity, and resiliency is the availability of good quality data and the ability to integrate these different approaches within a single solution. There are many aspects to this issue. One clear starting point to address these is knowledge about the data standards currently in use. This Report is intended to provide a partial overview of standards in common use as of the date of the report. We hope that it is useful to those interested in this area.
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 | 0.070 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 0.018 |
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".