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Record W2766166281 · doi:10.1111/nph.14860

The Sphagnome Project: enabling ecological and evolutionary insights through a genus‐level sequencing project

2017· article· en· W2766166281 on OpenAlexaff
David J. Weston, Merritt R. Turetsky, Matthew G. Johnson, Gustaf Granath, Zoë Lindo, Lisa R. Belyea, Steven K. Rice, DAVID HANSON, Katharina A. M. Engelhardt, Jeremy Schmutz, Ellen Dorrepaal, E. S. Euskirchen, Hans K. Stenøien, Péter Szövényi, Michelle C. Jackson, Bryan Piatkowski, Wellington Muchero, Richard J. Norby, Joel E. Kostka, Jennifer B. Glass, Håkan Rydin, Juul Limpens, Eeva‐Stiina Tuittila, Kristian K Ullrich, Alyssa A. Carrell, Brian W. Benscoter, Jin‐Gui Chen, Tobi A. Oke, Mats B. Nilsson, Priya Ranjan, Daniel Jacobson, Erik A. Lilleskov, R. S. Clymo, A. Jonathan Shaw

Bibliographic record

VenueNew Phytologist · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsWestern UniversityUniversity of Guelph
FundersU.S. Department of Energy
KeywordsEcologyNiche constructionBiologyAdaptation (eye)GenomicsEcosystemEvolutionary ecologyGenomeGene

Abstract

fetched live from OpenAlex

Considerable progress has been made in ecological and evolutionary genetics with studies demonstrating how genes underlying plant and microbial traits can influence adaptation and even 'extend' to influence community structure and ecosystem level processes. Progress in this area is limited to model systems with deep genetic and genomic resources that often have negligible ecological impact or interest. Thus, important linkages between genetic adaptations and their consequences at organismal and ecological scales are often lacking. Here we introduce the Sphagnome Project, which incorporates genomics into a long-running history of Sphagnum research that has documented unparalleled contributions to peatland ecology, carbon sequestration, biogeochemistry, microbiome research, niche construction, and ecosystem engineering. The Sphagnome Project encompasses a genus-level sequencing effort that represents a new type of model system driven not only by genetic tractability, but by ecologically relevant questions and hypotheses.

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
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.150
Threshold uncertainty score0.999

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 studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.071
GPT teacher head0.297
Teacher spread0.227 · 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

Citations68
Published2017
Admission routes1
Has abstractyes

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