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Record W4312226527 · doi:10.3390/plants12010143

The Impact of N.I. Vavilov on the Conservation and Use of Plant Genetic Resources in Scandinavia: A Review

2022· review· en· W4312226527 on OpenAlexaff
Svein Øivind Sølberg, И. Г. Лоскутов, Line Breian, Axel Diederichsen

Bibliographic record

VenuePlants · 2022
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicGenetic and Environmental Crop Studies
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsGeographyDomesticationDiversity (politics)IcelandicArchaeologyHistoryEconomic historyEcologyBiologyPolitical scienceLaw

Abstract

fetched live from OpenAlex

In this review we examine Nikolai Ivanovich Vavilov’s relationship to Scandinavia and the impact he and his ideas have had on Scandinavia. We trace the historical connections from Vavilov back to 18th century scientists, such as Carl Von Linneaus (Sweden) and 19th century European scientists such as Alphonse de Candolle (Switzerland), Henry de Vilmorin (France), and William Bateson (England). Vavilov has influenced the conservation work in Scandinavia resulting amongst other in the establishment of the Nordic Gene Bank in 1979 and the Svalbard Global Seed Vault which started operating in 2008. Vavilov travelled to Scandinavia in 1921 and in 1931 to give lectures and exchange ideas, especially with the breeders at the Swedish Seed Association (Svalöf) in Scania, Sweden, but also at the Copenhagen University in Denmark. Vavilov did not recognize Scandinavia as part of a center of origin of cultivated plants. It was only after World War II, when P.M. Zhukovsky, a scholar of N.I Vavilov, developed the concept of mega-centers of diversity of cultivated plants, that Scandinavia became part of what he termed the European-Siberian Region of Diversity. We list species domesticated in Scandinavia or Northern Europe, and we further discuss concepts related to crop evolution and highlight the great impact Vavilov has had by inspiring scientists across disciplines and over many decades.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.988
Threshold uncertainty score0.132

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.122
GPT teacher head0.276
Teacher spread0.154 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations6
Published2022
Admission routes1
Has abstractyes

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