2020-January-01-Dr Ingrid Luffman honored with Distinguished Alumni Award
Bibliographic record
Abstract
JOHNSON CITY – Dr. Ingrid Luffman, an assistant professor in the Department of Geosciences at East Tennessee State University, was recently honored with a 2019-2020 Distinguished Alumni Award from the Department of Geography at the University of Tennessee-Knoxville. Luffman, who graduated from UTK in 2013 with a Ph.D. in geography with a concentration in spatial analysis and watershed dynamics, received the award in the Early Career category for individuals who graduated within the past 10 years. Awards are also given in Mid-Career (10-25 years since graduation) and Lifetime (more than 25 years since graduation) categories. “The award is a great honor, and my sincere thanks go to the faculty at UTK Geography who challenged and guided me during my time there,” Luffman said. “Upon reflecting on the past few years during grad school and since graduation, I identified three ways the department helped to nurture students. “First, they inspired students by leading by example. Second, they included students in departmental activities so that we developed professional and personal relationships with faculty and students who now form part of our professional network. Third, they challenged students to explore areas of interest, present at conferences, and submit manuscripts for publication. These helped to set me off on solid footing to succeed in academia. “My sincere thanks go to two professors who had a substantial impact on my success, my advisor Dr. Liem Tran and the then-chair of the department, Dr. Carol Harden.” Luffman also holds a B.S. in mathematics and science and an M.S. in earth sciences from the University of Ottawa in Canada. She joined the ETSU faculty in 1997 and teaches courses in physical geography, geomorphology, hydrology, natural resources management and spatial analysis. She also co-teaches the Department of Geosciences’ field experience course and has led students on trips to such varied locations as the Colorado Plateau, Puerto Rico, the Rio Grande Valley, Hawaii and Ontario, Canada. Luffman’s main areas of research are karst hydrology, watershed restoration, soil erosion and medical geography. Among her most visible projects is a “Citizen Scientist” effort using crowdsourced data on water depth from visitors to Johnson City’s Founders Park to analyze how Brush Creek responds to rainfall events. Through her involvement with the Boone Watershed Partnership, which she has served as vice president, she has been instrumental in stream restoration projects at Sinking Creek and Beaver Creek. In addition, she and Dr. Arpita Nandi, chair of the Department of Geosciences, have several ongoing research projects at ETSU’s Eastman Valleybrook Campus.
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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) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".