The Impact of N.I. Vavilov on the Conservation and Use of Plant Genetic Resources in Scandinavia: A Review
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".