Looking to the future of soil biodiversity: the legacy of Diana Wall
Bibliographic record
Abstract
Diana Harrison Wall passed away on 25 March 2024 in Fort Collins, CO, USA, her home for more than 25 years and where she was the Director of the School of Global Environmental Sustainability (SoGES) and Distinguished Professor of Biology at Colorado State University (CSU) after having been faculty at the Natural Resource Ecology Laboratory (NREL) there for more than a decade. While Diana was to become respected globally for her work in soil ecology, soil biodiversity, and sustainability science, she began her career as a nematologist, receiving her PhD from the University of Kentucky-Lexington in plant pathology. When faced with the challenges of being a woman scientist in a male-dominated era, Diana blazed trails. Undoubtedly, her early-career years were formative. The meticulousness and focus she honed-in her science during these early years extended to the expectations she held for her mentees, and in her approach to leadership as she progressed in her career. Those of us who were mentored by Diana wish to honor her and her legacy by providing a glimpse of not only her research and achievements, but what lessons she left us, her mentees, to carry with us through our careers and lives. Those of us who were close collaborators and co-editors wish to honor Diana’s exemplary approach to science, forward-looking approach, and impact on the science community as a whole.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".