Chemistry shines in ISEF competition
Bibliographic record
Abstract
Han Jie (Austin) Wang, 18, of David Thompson Secondary School, in Vancouver, British Columbia, took home the top prize, the $75,000 Gordon E. Moore Award, at the Intel International Science & Engineering Fair (ISEF) held May 8–13 in Phoenix. His project, “Boosting MFC Biocatalyst Performance: A Novel Gene Identification and Consortia Engineering Approach,” used genetically engineered Escherichia coli to develop a more efficient and inexpensive microbial fuel cell. ISEF is the world’s largest precollege science competition. More than 1,700 high school students from more than 75 countries, regions, and territories competed for approximately $4 million in prizes. ISEF is organized by the Society for Science & the Public and jointly funded by Intel and the Intel Foundation. Additional awards are given by corporate, academic, governmental, and science-focused organizations, including the American Chemical Society. In addition to the top prizes, awards were handed out in 22 subject categories, including chemistry, biochemistry,
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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.024 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.208 | 0.125 |
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".