Alltech Young Scientists in #ONE19 Spotlight
Bibliographic record
Abstract
ONE: The Alltech Ideas Conference (ONE19) brought together some of the brightest international minds in science as part of the Alltech Young Scientist (AYS) competition. Four regional AYS winners were selected but only three were able to make it to the conference. Asia region winner Bing Liu, Jiangnan University, China was unable to attend. The others were: North America – Deeksha Shetty, University of Saskatchewan, Canada South America – Renata Oselame Nobrega, Universidade Federal de Santa Catarina in Brazil Europe/Africa – Anouschka Middelkoop, Wageningen University, the Netherlands Shetty was selected as the global 2019 Alltech Young Scientist after presenting her research to a panel of international judges as the final stage of the world’s largest agriscience competition for graduate university students. Winnings include $10,000, career mentorship and networking opportunities with innovative scientists from around the world. Now in its 14th year, the AYS competition has received entrants from a total of 120 universities and 40 countries.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.345 | 0.169 |
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".