Bibliographic record
Abstract
Torturing pumpkins A chemistry lab and a haunted house have a lot in common: strange vapors, mysterious transformations, and eerie glows. And like a ghost, a chemist doing demonstrations seeks to draw the audience in deeper. We asked our readers for their favorite #spookychem demonstrations to tempt lost travelers across the veil. Puking pumpkin: Chemistry teacher Michael Ng delights audiences at the annual Halloween Spooktacular held at Paul Kane High School in St. Albert, Alberta, with a riff on “elephant toothpaste.” He puts 400 mL of 35% H2O2, a squeeze of liquid dish soap, and several drops of food coloring in a 500-mL Erlenmeyer flask, which he places inside a carved pumpkin. Then he adds 100 mL of 2 M KI or NaI solution and holds down the gourd’s lid as a thick, frothy foam comes bursting from the pumpkin’s carved features. Dark-side pumpkin: Readers Scott Milam and Tom Kuntzleman
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.624 | 0.228 |
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".