PREDICTION OF LIFESPAN OF RAILWAY BALLAST AGGREGATE ACCORDING TO MECHANICAL PROPERTIES OF IT
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
As the railway lifespan is the main criterion for selection of the aggregate for ballast and for planning the maintenance of the railroad, it is important to define the relationship between the particle load resistant characteristics and a lifetime of ballast in structure. Assessment of the quality of the ballast aggregate particles under dynamic and static loading reflect both, the toughness and hardness, and these are identified with the Los Angeles Abrasion and Micro-Deval Abrasion values. The model formerly developed by Canadian Pacific Railroads was adapted to predict possible loads expressed in cumulated tonnes. Different ballast aggregate mixtures were tested in the laboratory including dolomite and granite. Calculated potential gross tonnage (expressed in Million Gross Tonnes) of the railway per lifetime for each different aggregate type presented. The outcome of this research is established classification system of railway ballast aggregate and defined Los Angeles Abrasion and Micro-Deval Abrasion values of aggregate dependently on required lifetime.
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) | 0.000 | 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