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
Freight railroads operating in the United States plan to spend a record $13 billion this year to expand, upgrade or repair their infrastructure. The figure could even approach $14 billion if several Class 1’s post strong enough financial performance in the first quarter of this year to warrant a capital spending increase, or if Congress extends the short-line tax credit through calendar year 2012. Class 1’s and other freight roads in Canada have also boosted their 2012 capex budgets. The article shows how many railroads plan to take on an ambitious number of projects this year because there is a significant amount of capital allocated for maintenance-of-way (MOW). The challenge is to determine ways to complete MOW with the increase in traffic. Track time is even tighter this year because U.S./Canadian carloads have increased and intermodal loads have increased as well. The article describes some of the many projects the major railroads are undertaking in the future and how they plan to get more productivity and efficiently out of available resources to complete the work quickly and safely.
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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