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Record W6889559186 · doi:10.25674/434

Looking to the future of soil biodiversity: the legacy of Diana Wall

2024· article· en· W6889559186 on OpenAlexaff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicConservation, Ecology, Wildlife Education
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsHonorSustainabilityWishResource (disambiguation)Natural resource

Abstract

fetched live from OpenAlex

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.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.011
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0040.003
Scholarly communication0.0110.006
Open science0.0010.003
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0100.004

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.

Opus teacher head0.118
GPT teacher head0.464
Teacher spread0.346 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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