Bibliographic record
Abstract
This thesis consists of two main projects and a third project which is provided in the appendix. The contribution of the first project, is a tool set for parallel random number gen- eration on GPUs in R, namely, the clrng package. This package is currently the only R package that provides facilities for generating random numbers in parallel on a GPU. It enables reproducible research by setting random initial seeds for streams on GPU and CPU. The random number generator in clrng guarantees independent parallel samples even when R is used interactively in an ad-hoc manner, with sessions being interrupted and restored. This package can be easily incorporated in other R packages, allowing developers to develop other types of random number generators or accelerate suitable computations in statistical applications. The contribution of the second project, is a methodology and software for creating profile likelihoods for parameters in Gaussian spatial models with Mat ́ern family of correlation functions, including anisotropic models. This methodology adopts a novel reparametrization for generation of representative points, and is implemented in the software gpuLik, which uses GPUs for parallel profile likelihoods computation. Our work has shown how GPU’s enable likelihood-based inference for Gaussian spatial models without requiring approximations to the variance matrix, even for moderately large datasets. The third project provided in the Appendix is dedicated to evaluating the impact of the COVID-19 pandemic on online public interest in various cancers (breast, colon, lung, prostate, and thyroid). A cross-sectional retrospective study was conducted utilizing Google Trends aggregate anonymous online search data from Canada. Welch’s Two Sample t-tests were performed and Benjamini-Hochberg procedure was used to correct for multiple comparisons.
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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 | 0.008 | 0.054 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.228 | 0.159 |
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".