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Record W3037648836 · doi:10.1139/cjps-2019-0286

Peduncle breaking resistance: a potential selection criterion to improve lodging tolerance in oat

2020· article· en· W3037648836 on OpenAlexaffvenue
Alireza Nakhforoosh, Shiv Kumar, T. Fetch, J. Mitchell Fetch

Bibliographic record

VenueCanadian Journal of Plant Science · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCrop Yield and Soil Fertility
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsPeduncle (anatomy)CultivarPaniclePlant stemSelection (genetic algorithm)BiologyAgronomyTraitAvenaHorticultureComputer science

Abstract

fetched live from OpenAlex

Breeding for tolerance to lodging is an objective, but also a challenge, in oat (Avena sativa L.) breeding programs. A widely adopted method to assess breeding lines for tolerance to lodging is based on visual scoring of plant standability (1 = standing upright; 9 = completely lodged). The lack of sufficient lodging pressure due to weather or growing conditions often renders the visual scoring method ineffective. We present an alternative approach that allows selection for tolerance to stem lodging by screening for peduncle strength in the absence of lodging pressure. This approach also provides objective selection of lodging tolerance using a quantitatively measurable plant trait rather than subjective scoring of the lodged plants. Stem structural and mechanical properties of six oat cultivars with varying levels of lodging tolerance were tested at field experiments over 3 site-years under three nitrogen rates. Results suggested peduncle breaking resistance (PBR), measured below the panicle, as a potential selection criterion for stem strength and therefore lodging tolerance. Significant genetic variation among oat cultivars (p < 0.01) was observed for PBR, which was significantly correlated with the strength of all lower internodes in all environments (R 2 > 0.73, p ≤ 0.05). This suggests that PBR provides a good estimation of the whole culm strength. Phenotyping of PBR can be easily integrated into breeding programs because of the ease of sampling and rapid measurement.

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 score0.950
Threshold uncertainty score0.887

Codex and Gemma teacher scores by category

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

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 head0.014
GPT teacher head0.205
Teacher spread0.191 · 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

Citations7
Published2020
Admission routes2
Has abstractyes

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