Bibliographic record
Abstract
Nicholas Stiles Wilkins University of Alberta, Edmonton, Alberta, Canada Nicholas Stiles Wilkins is currently working in the R&D team at Svante Inc. (Burnaby, BC) as an Adsorption Process Development Engineer. He received his PhD (Chemical Engineering) in 2022 at the University of Alberta under the supervision of Dr. Arvind Rajendran and Dr. Steven M. Kuznicki. His dissertation focused on gas-phase adsorptive separations, primarily on developments to dynamic column breakthrough methodology. In 2017, he also received a MSc (Chemical Engineering) at the University of Alberta under the supervision of Dr. Arvind Rajendran. In 2015, he received his BS in Chemical and Biochemical Engineering, with a minor in Computational & Applied Mathematics, at the Colorado School of Mines (Golden, CO). Nicholas is an active member of the International Adsorption Society, where he contributes to the society's education committee, primarily by organizing webinars, student conferences, and tutorial papers. His research interests include gas and vapour phase competitive adsorption equilibrium, gas diffusion in nanoporous materials, pressure/temperature swing adsorption process design and optimization, and adsorptive carbon capture.[1] Nicholas Stiles Wilkins: Writing – original draft; writing – review and editing. Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.
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 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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.369 | 0.290 |
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".