MétaCan
Menu
Back to cohort
Record W2946490751 · doi:10.1111/pbr.12709

Marker‐trait association analysis for postharvest needle retention/abscission in balsam fir (<i>Abies balsamea</i>)

2019· article· en· W2946490751 on OpenAlexafffundabout
Sherin Jose, Rajasekaran R. Lada

Bibliographic record

VenuePlant Breeding · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicHorticultural and Viticultural Research
Canadian institutionsNova Scotia Department of AgricultureDalhousie University
FundersAtlantic Canada Opportunities Agency
KeywordsBalsamBiologyAbies balsameaAbscissionChristmas treeSingle-nucleotide polymorphismPopulationSNPTraitBotanyHorticultureGenotypeGeneticsGeneMedicine

Abstract

fetched live from OpenAlex

Abstract Balsam fir the principal tree species of the Christmas tree industry, is a major export commodity of Atlantic Canada region. However, postharvest needle abscission poses a main concern leading to low consumer satisfaction and a shift in preference towards artificial trees. The search for diverse balsam fir lines with high needle retention is very crucial for quality improvement programmes. In this regard, a panel of 75 balsam fir genotypes exhibiting diversity for needle retention traits were randomly selected from a set of 220 genotypes evaluated for needle retention characteristics for two consecutive years (2012 and 2013) and genotyped with 41 bi‐allelic single nucleotide polymorphism (SNP)/insertion/deletion markers. The cluster analysis and population structure identified four and seven subpopulations. We observed an SNP marker with significant association ( p &lt; 0.05) with the needle retention/abscission trait by using general linear model and mixed linear model approaches. The present study lays the foundation for analysing the genetic mechanisms underlying needle retention/abscission, as well as the use of molecular markers to target specific traits in balsam fir.

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)0.001
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 score0.550
Threshold uncertainty score0.761

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.044
GPT teacher head0.253
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 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

Citations2
Published2019
Admission routes3
Has abstractyes

Explore more

Same venuePlant BreedingSame topicHorticultural and Viticultural ResearchFrench-language works237,207