Bibliographic record
Abstract
This dataset is supplementary material for the following paper: Peyman Shabani, Lucy Li, and Jeremy Laliberte. "Low-velocity impact (LVI) and compression after impact (CAI) of Double-Double composite laminates." Composite Structures (2024): 118615. https://doi.org/10.1016/j.compstruct.2024.118615 The Double-Double Laminate Finder is a computer tool developed based on Classical Lamination Theory (CLT) to find Double-Double (DD) composite layups that are equivalent in in-plane stiffness [A] or flexural stiffness [D] to conventional quadriaxial (QUAD) layups. This tool can also be used to evaluate material homogenization conditions, determine the optimal DD stacking sequence for homogenization, and indicate the QUAD and DD layups in the in-plane and flexural design range plots. The code is written in Python and runs on computers using the Windows operating system. Link to GitHub repository of this tool: https://github.com/Lich-NRC/DoubleDoubleCompDesign
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.009 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.552 |
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; both teacher heads agree on what is shown here.
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".