Bibliographic record
Abstract
<div>Seventeen research posters were prepared and presented by student authors. The posters covered a wide breadth of works-in-progress and recently completed projects. Topics included a variety of body regions and injury scenarios:<ul> <li> <div>Biofidelity Corridors of Powered Two-Wheeler Rider Kinematics from Full-Scale Crash Testing Using Postmortem Human Subjects, Meringolo et al.</div> </li> <li> <div>Cervical Vertebral and Spinal Cord Injuries Remain Overrepresented in Rollover Occupants, Al-Salehi et al.</div> </li> <li> <div>The Effect of Surfaces on Knee Biomechanics during a 90-Degree Cut, Rhodes et al.</div> </li> <li> <div>Investigating the Variabilities in the Spinal Cord Injury in Pig Models Using Benchtop Test Model and Ultrasound Analyses, Borjali et al.</div> </li> <li> <div>Relationship between Tackle Form and Head Kinematics in Youth Football, Holcomb et al.</div> </li> <li> <div>Comparing Motor Vehicle Collision Injury Incidence between Pregnant and Nonpregnant Individuals: A Case–Control Study, Levine et al.</div> </li> <li> <div>Development of an Automated Pipeline to Characterize Full Rib Cage Shape Variability, Robinson et al.</div> </li> <li> <div>Soft Tissue Force Attenuation and Redistribution during Lateral Hip Impacts, Pretty et al.</div> </li> <li> <div>Hybrid III Small Female Neck Interaction with a Driver Airbag: Preliminary Observations, Boyle et al.</div> </li> <li> <div>Changes in Youth Football Athletes’ Oculomotor Task Metrics across Three High School Seasons of Play, Pang et al.</div> </li> <li> <div>Measurement of Shielding Stiffness in Ice Hockey, Vakili et al.</div> </li> <li> <div>Investigating the Relationship between Vehicle-Based and Biomechanics Injury Metrics in Car-to-End Terminal Crashes Using a Human Finite Element Model, Buckland et al.</div> </li> <li> <div>On-Field Instrumented Mouthguard Coupling, Luke et al.</div> </li> <li> <div>Investigation of Rear-Seat Occupant Safety during High-Speed Frontal Crashes Using GHBMC M50-O, Dahiya et al.</div> </li> <li> <div>Deformable Headform Design Choices: An Evaluation of Brain Simulant Stiffness Influence on Intracranial Displacements and Strain, Xu et al.</div> </li> <li> <div>Changes in Neurocognitive Outcomes among Youth Football Teams Participating in an Intervention, Marks et al.</div> </li> <li> <div>A Parametric Skeleton Model of Human Upper Extremities Accounting for Morphological Variations among the Diverse Population, Neeluru et al.</div> </li> </ul> </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.
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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".