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Record W4291902436 · doi:10.1016/j.isci.2022.104848

Estimating biodiversity across the tree of life on Mount Everest’s southern flank with environmental DNA

2022· article· en· W4291902436 on OpenAlexaff
Marisa Lim, Anton Seimon, Batya R Nightingale, Charles C.Y. Xu, Stephan Halloy, Adam J. Solon, Nicholas B. Dragone, Steven K. Schmidt, Alexander M. Tait, Sandra Elvin, Aurora C. Elmore, Tracie A. Seimon

Bibliographic record

VenueiScience · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental DNA in Biodiversity Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsBiodiversityEnvironmental DNAEcosystemEcologyGlacial periodAeolian processesPhylumClimate changeGeographyPhysical geographyTaxonBiologyPaleontology

Abstract

fetched live from OpenAlex

Species composition in high-alpine ecosystems is a useful indicator for monitoring climatic and environmental changes at the upper limits of habitable environments. We used environmental DNA (eDNA) analysis to document the breadth of high-alpine biodiversity present on Earth's highest mountain, Mt. Everest (8,849 m a.s.l.) in Nepal's Khumbu region. In April-May 2019, we collected eDNA from ten ponds and streams between 4,500 m and 5,500 m. Using multiple sequencing and bioinformatic approaches, we identified taxa from 36 phyla and 187 potential orders across the Tree of Life in Mt. Everest's high-alpine and aeolian ecosystem. These organisms, all recorded above 4,500 m-an elevational belt comprising <3% of Earth's land surface-represents ∼16% of global taxonomic order estimates. Our eDNA inventory will aid future high-Himalayan biomonitoring and retrospective molecular studies to assess changes over time as climate-driven warming, glacial melt, and anthropogenic influences reshape this rapidly transforming world-renowned ecosystem.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.002
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.014
GPT teacher head0.200
Teacher spread0.186 · 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 teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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

Citations7
Published2022
Admission routes1
Has abstractyes

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