Bibliographic record
Abstract
Edna Matta-Camacho remembers being a very inquisitive girl who used to open radios or hair dryers with a screwdriver to find out how they worked. Occasionally, she would break things in the process, to the disappointment of her mother. “But at the same time, she supported my curiosity,” Matta-Camacho says. Today, she’s just as curious. But instead of tinkering with household objects, Matta-Camacho is now a senior assessment officer at the Pharmaceutical Drugs Directorate of Health Canada, where she ensures that new drugs have fulfilled preclinical and clinical requirements of safety and efficacy to meet regulatory standards. She also created a foundation that aims to stimulate the scientific curiosity of girls and young women in rural Colombia, just like her mother did for her. Matta-Camacho was first introduced to chemistry at the age of 14. “It was like love at first sight,” she recalls. She was captivated by the idea
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) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".