Bibliographic record
Abstract
Since its founding in 2018, the Journal of the Natural Sciences (JNS) has remained committed to fostering a meaningful and accessible publication experience for student researchers.With each new issue, we continue to build on this vision by showcasing the curiosity, rigor, and creativity of the undergraduate and graduate research community at the University of Toronto Scarborough.This issue highlights the diversity of scientific inquiry at UTSC, featuring work that spans multiple disciplines and reflects the dedication of our authors to sound methodology and thoughtful analysis.Through JNS's double-blind peer-review process, contributors not only share their findings with a broader audience but also engage deeply with the collaborative and constructive nature of scientific scholarship.The publication of this issue would not have been possible without the collective efforts of many individuals.We would like to extend sincere thanks to our faculty advisors for their continued mentorship, to our reviewers and editors for their careful attention and professionalism, and to the executive team for their leadership and commitment to excellence.As JNS continues to grow, we remain dedicated to serving as a platform where emerging researchers can develop their skills, exchange ideas, and contribute meaningfully to the scientific community.We hope that this issue inspires readers and encourages future authors to share their work with us.
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".