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 article suggests that the market for freight cars will continue to be brisk in 2006 if the first quarter of the year is any indication. Last year’s new-car acquisitions totaled more than 68,000, the largest since 1999 and an increase of 46.4 percent over 2004’s total. During the first quarter of 2006, rail-car deliveries totaled 18,542. If that pace continues, there could be more than 74,000 cars delivered this year. The reasons for the run on rail cars include a relatively good U.S. economy, growth in the intermodal, coal and merchandise segments of the market, and more truck-to-rail diversions. Ethanol production has also led to a surge in orders. Included in the Fleet Stats ’06 are the following statistics: 1) selected car fleet data; 2) railroad car owners; 3) private car owners; 4) changes in the U.S. freight car fleet; 5) U.S. freight cars by type and age; and, 6) Class I locomotives.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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