Bibliographic record
Abstract
As Eureka Undergraduate Science Journal grows as a multidisciplinary publication and resource for undergraduate students, we continue to collect unique perspectives on what journeys through science can look like.We have had the pleasure of interviewing a range of scientists, from science communicators to summer researchers to Nobel laureates.Each of these featured scientists has pioneered their own unique path.Their experiences tell that science isn't made up of isolated disciplines; each discipline exists in a continuum with the others.This collection of Eureka's recently published articles reflects the diversity of research undertaken by undergraduate students in neuroscience, psychology, and microbiology.The editors would like to invite you to read this issue across the boundaries of discipline, drawing from the experiences and findings of adjacent fields to inform your own thinking.Eureka's virtual Undergraduate Research Symposium in June 2021 was an exciting forum of discussion and showcase of undergraduate enthusiasm for science with this multidisciplinary spirit.Attendees learned about approaches to questions in neurophysiology, science ethics, oncology, mathematics, and beyond.The next generation of scientists brings hope and excitement to the research stage.Eureka is pleased to support and train these trailblazers as they make discoveries and build bridges between schools of thought.Not everyone has the same path, but we want to support you in yours.As this issue's featured scientist Dr. Torah Kachur advised, "Say yes to opportunities that come along...you never know what they're going to be".Dr.
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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 | 0.014 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.018 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.058 | 0.054 |
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".