A NEW APPARATUS TO SIMULATE ATHLETIC FIELD TRAFFIC: THE CADY TRAFFIC SIMULATOR
Bibliographic record
Abstract
Realistic traffic simulation is crucial to the validity of athletic field research. Previously developed athletic field traffic simulators contain studded drums that turn at different speeds, creating shear forces at the playing surface. The Cady Traffic Simulator (CTS) (a modified walk‐behind core cultivation unit) was developed at Michigan State University in 2000. The objective of this study was to compare the magnitude and direction of the forces produced by two traffic simulators: the Brinkman Traffic Simulator (BTS), a pull‐behind unit, and the CTS. Both simulators were operated over an in‐ground force plate, which measured the forces in three directions: front to back, side to side, and vertical. The CTS produced a higher compressive stress and net shear stress when operated in either direction than the BTS. The average peak compressive stress produced by the feet of the CTS when operated in the forward direction was approximately 30 times higher than the combined compressive stresses of both BTS drums. The average peak net shear stress produced by the feet of the CTS when operated in the forward direction was approximately 15 times higher than the combined net shear stresses of both BTS drums. Operating in the reverse direction, the average peak compressive stress produced by the feet of the CTS was greater than five times the compressive stresses of both BTS drums combined. The average peak net shear stress produced by the feet of the CTS was approximately four times higher than the combined net shear stresses of both BTS drums.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".