A Methodology to Evaluate Strain Within Tissue Slabs under Complex Loading
Bibliographic record
Abstract
The biomechanical response of brain tissue to strain and the immediate neural outcomes are of fundamental importance in understanding brain injury.Experimental work on measuring the strain-response of brain tissue must be completed to bridge this gap.The objective of this work was to develop and test a headform model for impact that has the capacity to incorporate porcine brain tissue.A surrogate tissue slab was used in the current work, made from silicon gel, filled with radio-opaque markers.The deformation was monitored using a high-speed in-situ X-ray cinematography system at 7,500 FPS.The kinematics and strain results from impact were compared among the impact speeds.Strain progression was clear throughout the slab with increased speed resulting in increased strain levels.Repeated impacts at the same speed displayed the region-specific repeatability of the headform under impact testing.iiiWhen I started my thesis back in the fall of 2019, I truly did not know the level of growth I was about to undertake over the course of my thesis.Not only due to academic pressure, but personal growth, contributed to by both the struggles that the pandemic brought and the things I learned from the people around me.First, I'd like to acknowledge my supervisor, Professor Oren Petel for guidance and support throughout my time at Carleton.Even the smallest bits of advice you would give helped to progress my work, and solve any issues in the lab.Getting the opportunity to work hands-on in a very collaborate space was inspiring.I would also like to acknowledge the support from the Department of the Army, U.S. Army Research Office on this and related projects I was able to work on while at Carleton.With this funding, all of the experimental work could be performed for this thesis.I want to point out the many people in our lab who helped me both in person and virtually when needed.To the many past and current graduate students in our lab whom I got the privilege to know, thank you for giving constructive feedback, advice, and overall contributing many good memories to my masters.I have to point out Sheng, Scott and Anton for their many helpful ideas and positive attitudes in the lab.I must also sincerely thank Jennifer and Ashley for listening to me rant about anything and everything, research and other.Last but not least, a big thank you to the people closest to me.To my immediate family including my parents, Terri and Scott, along with my brothers Matt and Kevin, thank you for always encouraging me to keep moving forward.A special shout out to Matt for letting
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".