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Potential Range of Bulbocodium versicolor (Ker-Gawl.) Spreng. (Colchicaceae, Liliopsida) in Russia

2020· article· en· W3041461889 on OpenAlexaboutno aff
А. С. Кашин, А. С. Пархоменко, Л. В. Куликова, Н. А. Петрова, И. В. Шилова, M. V. Lavrentiev, V. A. Shushunov

Bibliographic record

VenuePovolzhskiy Journal of Ecology · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)St petersburgPrecipitationRange (aeronautics)GeographyPhysical geographyForestryEnvironmental scienceArchaeologyMeteorologyRussian federationRegional science

Abstract

fetched live from OpenAlex

The article presents a bioclimatic model of the potential range of Bulbocodium versicolor in European Russia. To build the model, we analyzed a matrix containing 166 B. versicolor localities in the studied region; the analysis was carried out in the SDMtoolbox program using the climatic paramaters from the WorldClim open database. The model demonstrates that, given the available dataset on the modern climatic conditions, B. versicolor may occur in a wider geographical range comprising, at the very least, the Belgorod, Voronezh, Volgograd, Lipetsk, Penza, Rostov and Saratov provinces. Also, within European Russia, the most favorable conditions for B. versicolorare found in most of the Voronezh and Volgograd provinces as well as in some areas of the Right Bank and Left Bank of the Volga River adjacent to the Volga Upland (in the Saratov province). The maximum occurrence probability is 70–100% while the average occurrence probability is 40– 60%. The maximum contribution to the model is made by the precipitation of the warmest and most humid quarter (June–August); a smaller contribution is made by the average temperature of the coldest (December–February) and warmest (June – August) quarters as well as by the average annual precipitation. The least contribution is made by the precipitation of the most humid month (July) and the driest quarter (March–May). Finally, we conclude that bioclimatic model facilitates a better understanding of the geographical distribution of the species in question.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.228
Teacher spread0.209 · 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 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

Citations5
Published2020
Admission routes1
Has abstractyes

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