Bibliographic record
Abstract
Welcome to Issue 9 of the Sciential Journal!As we slowly start to see things going back to normal after a global pandemic, we're thrilled to publish a new issue with exciting research.Sciential gives students an opportunity to publish work that they are passionate and enthralled by.Our aim is to explore the interdisciplinarity of scientific fields through effective science communication.Over the years, especially during the pandemic, we have witnessed the importance of science communication and research.With this journal, we hope to shed light upon the accessibility and interdisciplinary nature of science.This issue explores a range of scientific topics: the impacts of air pollution, the qualities of glioblastoma multiforme, how the public perceived ADHD in adults, dexamethasone's connection to COVID-19, how effective lay summaries are in journals, the representation of men's mental health in the media, and lastly, the impact of science communication training.We are extremely grateful to the members of the Sciential team for their work and dedication to the journal.We would like to recognize the fantastic work done by our Senior Editors, Samini Hewa and Zani Zartashah, for overlooking the peerreview process.Additionally, we would like to acknowledge the incredible work ethic of our Editors, for their continuous determination to make sure our journal is of high standard.We also appreciate the remarkable work done by the creative board, led by our Creative Director Zachary de Guzman.Lastly, we would like to acknowledge the founders of Sciential,
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.021 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.560 | 0.494 |
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".