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Record W2013551898 · doi:10.2135/cropsci2004.1601

Stability of Nonflowering Orchardgrass

2004· article· en· W2013551898 on OpenAlexaff
Michael D. Casler, Randolph Barker, J. H. Cherney, Y. A. Papadopolous

Bibliographic record

VenueCrop Science · 2004
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicHorticultural and Viticultural Research
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsDactylis glomerataBiologyPastureTemperate climateCultivarAgronomyTraitGrazingPanicleBotanyHorticulturePoaceae

Abstract

fetched live from OpenAlex

Orchardgrass ( Dactylis glomerata L.) is a valuable pasture species in much of temperate humid North America. However, profuse and early flowering in spring creates management problems for graziers and reduces intake of livestock in a management‐intensive rotational grazing system. The objectives of this study were to estimate environmental stability, genotypic variability, and frequency of nonflowering and sparse‐flowering plants in two sparse‐flowering orchardgrass populations. Seven cultivars and 299 half‐sib families were evaluated for 2 yr at five locations between 42° and 47°N latitude. Sparse‐flowering populations WO‐SF‐B and WO‐SF‐C were later in maturity, produced fewer panicles per plant, and had higher frequencies of sparse‐flowering and nonflowering plants than the cultivars. Plants had varying levels of expression of the nonflowering trait, ranging from slightly sensitive (sparse flowering in one year) to highly sensitive (stable nonflowering across years), with highly sensitive plants found only within populations WO‐SF‐B and WO‐SF‐C. The nonflowering trait of orchardgrass appears to be controlled by floral‐regulation genes that are turned off by short‐day temperatures below a critical threshold. Such a threshold appears to exist for all orchardgrass plants, but is increased in those plants expressing the nonflowering trait.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.199
Threshold uncertainty score0.297

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.054
GPT teacher head0.296
Teacher spread0.242 · 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 designBench or experimental
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

Citations11
Published2004
Admission routes1
Has abstractyes

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