Статистика внутреннего транспорта Европы и Северной Америки за 2022 год
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
This publication (volume LX) presents annual statistics on inland transport (road, rail and inland waterways). Data covers Europe, Canada and the United States. The 2022 edition provides a series of transport statistics tables for the 56 member States of UNECE. It brings together statistical information on all the modes of transport covered by the Inland Transport Committee (road, rail and inland waterways) for all member States of the ECE region. A short summary at the beginning of each chapter provides some key figures on each sector, followed by detailed data on each of the statistics sub-categories. The publication is purely statistical in character. Like the previous issues, it has been prepared by the Sustainable Transport Division of UNECE with the generous cooperation of national statistical offices. It is issued in accordance with the recommendation of the inland Transport Committee at its first session that the Division should regularly publish the most recent available data on transport for as many countries within the UNECE region as possible.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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