The Effect of Locomotive Horn Characteristics on Motorist Detection
Bibliographic record
Abstract
<p>Previous research examining locomotive train horns has suggested that certain characteristics, such as the number of flutes used and the frequency components of flutes, may enhance the effectiveness of the horn as a warning device. The current research sought to examine these findings by using recordings of standard train horns that varied in their sound characteristics. A signal detection procedure was used in which participants were asked to resp ond when they noticed a locomotive horn played through a background of in -car noise. Participants were also simultaneously engaged in a visual tracking task in order to impose additional attentional demands similar to those encountered by a motorist. Results of the study are largely consistent with previous tests of locomotive train horn efficacy; however, the effects of isolated characteristics in real horns appear to be interactive rather than additive. Additional findings also suggest that detection may also be dependent on whether the demands of driving and distractions leave sufficient attention left to detect and react to the train horn. Results are discussed in terms of implications for motorist safety and design of locomotive horns.</p>
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".