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Ecology and Evolution of the Sudden Oak Death Pathogen <i>Phytophthora ramorum</i>

2019· article· en· W2949693119 on OpenAlexafffund
Niklaus J. Grünwald, Jared M. LeBoldus, Richard C. Hamelin

Bibliographic record

VenueAnnual Review of Phytopathology · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Pathogens and Resistance
Canadian institutionsUniversité LavalUniversity of British Columbia
FundersAnimal and Plant Health Inspection ServiceGenome British ColumbiaNational Institute of Food and AgricultureAgricultural Research ServiceGenome CanadaU.S. Department of Agriculture
KeywordsPhytophthora ramorumBiologyLarchEcologyPhytophthoraBotany

Abstract

fetched live from OpenAlex

The sudden oak and sudden larch death pathogen Phytophthora ramorum emerged simultaneously in the United States on oak and in Europe on Rhododendron in the 1990s. This pathogen has had a devastating impact on larch plantations in the United Kingdom as well as mixed conifer and oak forests in the Western United States. Since the discovery of this pathogen, a large body of research has provided novel insights into the emergence, epidemiology, and genetics of this pandemic. Genetic and genomic resources developed for P. ramorum have been instrumental in improving our understanding of the epidemiology, evolution, and ecology of this disease. The recent reemergence of EU1 in the United States and EU2 in Europe and the discovery of P. ramorum in Asia provide renewed impetus for research on the sudden oak death pathogen.

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)0.000
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.007
GPT teacher head0.213
Teacher spread0.207 · 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
GenreReview

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

Citations94
Published2019
Admission routes2
Has abstractyes

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