Bibliographic record
Abstract
a r He a l t h -Wh y We Ne e d a n I n t e r s e c t i o n a l S e x a n d G e n d e r A p p r o a c h S t o p p i n g A g i n g : D r e a m o r R e a l i t y ?Welcome to Issue 8 of Sciential!As mentioned in the last few issues, the COVID-19 pandemic has made it difficult for people to gather and collaborate.It has also demonstrated and reinforced the importance of effective science communication and fostering a more scientifically literate world.Despite these conditions, at Sciential, we have committed ourselves to sustaining the collaboration that the journal generates and to continuing our responsibility of communicating science effectively.Like all our issues, Issue 8 explores a variety of topics: the gender and sex differences of cardiovascular health; neuron potentiation; the feasibility of preventing aging; black hole thermodynamics; male body dysmorphia; non-small lung cell carcinomas; and the lack of accessibility in scientific lay summaries.We are excited to present to you the incredible work that undergraduate students at McMaster University have produced in these interesting research areas and disciplines.We would also like to take a moment to congratulate and thank our incredible team for their unwavering commitment and phenomenal work on this issue.To our Senior Editors, Dalen Koncz and Lavanya Sinha, thank you for constantly upholding the standard of the journal and the work that we publish.A special thank you goes out to Angelina Lam and the rest of the Creative Board for their always extraordinary work ethic.Finally, to our editors, a thank you must be extended to recognize their dedication to ethical and proficient editing.The upcoming school
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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 | 0.010 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.020 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.017 | 0.011 |
| Insufficient payload (model declined to judge) | 0.283 | 0.203 |
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".