Equity, Diversity, Environment, and Society
Bibliographic record
Abstract
I am a third-year Ph.D. student in Yolanda Hedberg’s lab, studying at the University of Western Ontario.\nBasically, I use different instruments including ToF-SIMS, FESEM/STEM, and electrochemical measurements to analyze gold nanoparticles and nanoclusters and their interaction with biomolecules such as Cysteine in the physiological environment. I aim to investigate the possibility of using nanomaterials to increase the efficiency of radiation therapy and improve cancer treatment.\nI was one of the awardees in the first round of the CREAT CORRECT program and I aimed to go to the Chalmers University of Technology in Gothenburg, Sweden for my mobility. The purpose of my travel was to use NanoSIMS which could allow me to image my nanomaterials with a high resolution (down to 50 nm), and an excellent collection efficiency which is combined with sufficient mass resolution. In my research plan, the biggest opportunity for me would have been working with more nano instrumentation, collaborating with more scientists majoring in the same area, and learning new software and its instruments.\nIn this presentation I talked mostly about the equity principles and how it can be challenging for some of scientists! I shared my experience of being rejected to enter a country in Europe to continue my research for a while. and the fact that how that incident made me interested me in scientists’ obstacles through the history.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.036 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".