Bibliographic record
Abstract
On the first Earth Day, April 22, 1970, Mary Lou Guerinot decided that she would major in biology in college in the hope of promoting environmental sustainability. As the 50th anniversary of Earth Day approaches, Guerinot, now a professor of biological sciences at Dartmouth College and a member of the National Academy of Sciences, is well on her way toward achieving that goal through her work on the molecular mechanisms of metal ion uptake and its regulation. Guerinot’s work is laying the foundation for environmentally sustainable, nutrient-dense crops, as well as plant-based solutions for removal of toxic metals from soil. Her Inaugural Article (1) reports the identification of a transcription factor essential for plant growth under iron deficiency. The finding moves her team closer to understanding the iron homeostasis pathway in plants, holding promise for improving agricultural productivity and human health. Mary Lou Guerinot. Image courtesy of Olga Zhaxybayeva (Dartmouth College, Hanover, NH). Guerinot was raised in upstate New York, where she attended the Rochester-based Catholic all-girls St. Agnes High School. “Unlike the boys’ Catholic high school nearby, it was basically science-light,” she says. “We didn’t have physics or calculus, and very few students went on to college to study science.” She and a few other students urged their math teacher to let them study calculus on their own. The hard work on this subject and others paid off, and Guerinot was accepted at Cornell University’s College of Agriculture and Life Sciences. She adhered to her decision to major in biology and earned a Bachelor of Science degree with distinction at Cornell in 1975. Four years later, Guerinot earned a doctorate in biology from Dalhousie University under the direction of David Patriquin, studying the sea urchin–lobster–kelp ecosystem. Envisioning life as a marine microbiologist, Guerinot did a postdoctoral fellowship at the …
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.100 | 0.060 |
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".