2022-March-01-ETSU scientist playing on a global stage
Bibliographic record
Abstract
JOHNSON CITY, Tenn. (March 2022) – Late last month, Dr. Aruna Kilaru wrote a piece aimed at addressing food insecurity and climate change using agricultural biotechnology. Suggesting how both farmers and government agencies can work together to reduce harmful emissions and minimize food waste, the article appeared as part of her work as a science, technology and policy fellow of the American Association for the Advancement of Science, and science adviser and international trade policy officer for the U.S. Department of Agriculture’s Foreign Agricultural Service, New Technologies and Production Methods Divisions. Dr. Aruna Kilaru It means that Kilaru, a professor in the Department of Biological Sciences at East Tennessee State University, is playing an important role in the federal government’s climate, global hunger and food security initiative. “Addressing poverty, hunger and malnutrition, as well as the effects of extreme weather, is incredibly important both here at home and abroad,” said Kilaru. “I am honored to be part of it.” Federal and state organizations have taken notice of Kilaru’s work. In late 2021, The Tennessee Academy of Sciences presented her with the 2021 Distinguished Scientist Award. Kilaru’s work, wrote the Academy President Dr. Amy L. Thompson, “is central to the globally important tasks of feeding a growing world population and the production of clean biofuels to developing stress-tolerant crops with improved productivity.” Kilaru has ongoing international and national collaborations, another metric that helped her win the state honor. She works with groups in Australia, Canada, China, Germany and India, as well as organizations in Kansas, Louisiana, Massachusetts and Texas. Kilaru has earned over $1.1 million in grant funds since she joined ETSU in 2011. Other scientists have cited her work more than 1,500 times in the last decade. She is also the recipient of ETSU’s 2021 Distinguished Faculty Award in Research and the Notable Women Award.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.304 | 0.160 |
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".