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
<div class="section abstract"><div class="htmlview paragraph">Collision reconstruction often involves calculations and computer simulations, which require an estimation of the weights of the involved vehicles. Although weight data is readily available for automobiles and light trucks, there is limited data for heavy vehicles, such as tractor-semitrailers, straight trucks, and the wide variety of trailers and combinations that may be encountered on North American roads. Although manufacturers always provide the gross vehicle weight ratings (GVWR) for these vehicles, tare weights are often more difficult to find, and in-service loading levels are often unknown. The resulting large uncertainty in the weight of a given truck can often affect reconstruction results.</div><div class="htmlview paragraph">In Canada, the Ministry of Transportation of Ontario conducted a Commercial Vehicle Survey in 2012 that consisted of weight sampling over 45,000 heavy vehicles of various configurations. This paper analyzes that weight data according to the vehicle configuration, body style, and total number of axles. Results are presented for the empty and in-service weights of the surveyed trucks. Comparison of the results of this study to prior studies indicates that the results likely apply to most jurisdictions throughout North America.</div></div>
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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