Utilizing jackknife and bootstrap to understand tensile stress to failure of an epoxy resin
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A study was conducted on the tensile stress of an epoxy resin (Resoltech® 1050/1056). This was done by gathering a sample of 39 tensile strength data under consistent levels of stress. The tensile stress resistance is often characterized using a three-parameter Weibull distribution and the reliability of this characterization, given by confidence intervals (CIs). This approach commonly utilizes data-resampling techniques to estimate the CI of its parameters. CIs are constructed from six existing point-estimation methods. Herein, the jackknife was carried out to calculate the CIs using 39 subsamples and bootstrap methods using 100 or 200 subsamples. To date, there have been no studies exploring the effectiveness of subsampling methods for constructing CIs related to tensile strength. In this study, jackknifed and bootstrapped samples are used to implement the percentile method and three variations of the bias correction methods. We then performed simulations to evaluate the reliability of these methods using a Weibull random number generator. Our results showed that while the bias-corrected approach generated the most stable CIs from replicate samples, its accuracy was contingent on the point-estimation method employed. We also found that the different methods for calculating CIs resulted in significantly varying widths of the CIs.
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.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it