Bibliographic record
Abstract
Around the turn of this century, the automotive industry introduced a new type of technology to drive the gauges on a vehicle’s instrument cluster. The change was unannounced to the collision reconstruction world, but soon after, investigators observed a marked increase in crashed vehicles displaying frozen gauges at what often appeared to be correct readings. The new technology was the use of stepper motors which require power to return to the zero position. Hence if electrical power is lost, the gauges stop in position. There have been a number of previous papers covering the operation of the instruments and crash testing of cars and motorcycles to establish the ability of the instruments to withstand the forces on the instrument during a collision. This paper aims to compare the frozen instrument readings from real world collisions with the available EDR data from the crashed vehicles. With the assistance of the collision reconstruction community, a large dataset of 236 vehicles with frozen speedometer readings were compared with EDR and other corroborating methods. This paper reviews the current state of knowledge, compares the instrument readings of each of the 236 vehicles against the available EDR data or other corroborating method. It then assesses each case against the criteria proposed by Goddard and Price [3, 4] to assess if the cases that are being filtered out for selection, are accurate readings. It was found the existing criteria was an effective filter in removing the majority of cases capable of producing erroneous readings. However, it was found that some erroneous readings were present in low speed impacts. With the addition of a minimum speed criteria, the likelihood of an erroneous reading was greatly reduced. At recorded speedometer readings over 80 Km/h, the corroboration with the EDR speed had a standard deviation of 4%.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".