A crystal-growing contest and a Nobel Prize-winning inconvenience
Bibliographic record
Abstract
Crystal contest Crystals fill Jason Benedict’s office once a year. A chemistry professor at the University at Buffalo, Benedict runs the U.S. Crystal Growing Competition (bit.ly/2hwDBvl). This year, 83 entries came in from K–12 students and their teachers. The contest began three years ago, after Benedict heard of a similar effort in Canada. Starting in mid-October, contestants have about a month to grow the biggest and highest-quality crystals they can and send them to Buffalo. The raw materials are free to contestants thanks to sponsorships from several companies, nonprofits, and professional societies. This year and last, students used reagent-grade alum. Benedict says alum is a good choice because it is safe, it is one of the easiest crystals to grow, and the resulting crystals are shelf-stable. The sponsorships also allow Benedict to offer cash prizes of $50 to $200 for the winners. The judges pick winners in two categories: overall,
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".