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Record W2169807947 · doi:10.4141/cjps2013-062

Assessment of genetic diversity and differentiation of<i>Elymus nutans</i>indigenous to Qinghai–Tibet Plateau using simple sequence repeats markers

2013· article· en· W2169807947 on OpenAlexvenueno aff
Shiyong Chen, Xinquan Zhang, Xiao Ma, Linkai Huang

Bibliographic record

VenueCanadian Journal of Plant Science · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Pathogens and Resistance
Canadian institutionsnot available
Fundersnot available
KeywordsGenetic diversityBiologyPlateau (mathematics)ElymusBotanyBiological dispersalPoaceaePopulation

Abstract

fetched live from OpenAlex

Chen, S., Zhang, X., Ma, X. and Huang, L. 2013. Assessment of genetic diversity and differentiation of Elymus nutans indigenous to Qinghai–Tibet Plateau using simple sequence repeats markers. Can. J. Plant Sci. 93: 1089–1096. Elymus nutans Griseb., an important alpine forage grass, is widely distributed in the Qinghai–Tibet Plateau. A total of 50 E. nutans accessions from the eastern Qinghai–Tibet Plateau were analyzed using simple sequence repeats (SSR) markers from wheat and Elymus species. Our results show that a total of 144 reliable bands were generated, of which 132 (91.38%) were found to be polymorphic. Nei-Li's genetic similarity coefficients ranged from 0.515 to 0.870 with an average of 0.719, which shows a high level of genetic diversity and a broad genetic base among accessions. There was a low correlation between genetic distance and geographical distance (r=0.121, P=0.088) in the region, which is consistent with the unweighted pair group method with arithmetic average cluster analysis of accessions. The mountain ridges and river valleys in the eastern Qinghai–Tibet region could serve as genetic barriers for pollinator movement and seed dispersal. The rule of the most genetic diversity at medium altitude of E. nutans in the Qinghai–Tibet Plateau was also validated in the study. The implications of these results for the conservation of E. nutans are discussed.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.738
Threshold uncertainty score0.992

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 studies0.0010.000
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 head0.025
GPT teacher head0.215
Teacher spread0.190 · 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 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

Citations27
Published2013
Admission routes1
Has abstractyes

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