Bibliographic record
Abstract
This dissertation addresses the need for more robust and materially efficient structures, specifically structures that absorb higher levels of energy, can self-erect and which have variable reaction capabilities. These structures can take much higher levels of strain and loading compared to current technologies in reinforced concrete and carbon fiber composites. These structures are made using a novel method of joining segmental elements. A great advantage with this segmental structural system is it's survivability under overloaded conditions. During an impact, or concentrated pressure load, the structure will wrap around the impact zone thereby widely distributing the contact stresses. This function or joining-method can be adapted to vehicles, or aircraft frames and other structures such as landing strips, bridges and buildings. Self-erecting structures are also a function achieved by this innovation. This may be accomplished through the process of post-tensioning. A structure that is in a collapsed state but linked with tendons is pulled into an erect state as the tendons are tensioned. One example is a tower, which would slowly pull itself into a standing position one segment at a time as the tendon tension increased. At a fully prestressed state the tendons would be anchored. Additionally, variable reactivity to loading can be incorporated into each joint function. The joint structures incorporate rubber layers and end caps which are at the ends of each segment. The rubber layers are similar in function to the hyaline cartilage found throughout animal skeletal structures.
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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 | 0.000 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".