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Record W7113542333

Proportion of Leaves and Floral Tillers and Nutritional Quality of Prolific and Non-Prolific Seed Yielding Cultivars of Smooth Bromegrass

2025· article· W7113542333 on OpenAlexaboutno aff

Bibliographic record

VenueUKnowledge (University of Kentucky) · 2025
Typearticle
Language
FieldEnvironmental Science
TopicTurfgrass Adaptation and Management
Canadian institutionsnot available
Fundersnot available
KeywordsCultivarPastureGrazingTemperate climateEcotypeGrowing season
DOInot available

Abstract

fetched live from OpenAlex

In the semi-arid clima\e of the Canadian Prairies most herb­age production occurs in May and June whereas the grazing season lasts until October. Summer and fall pasture consists largely of mature, quiescent vegetation with senescent leaves and lignified stems. Floral tillers of some temperate grasses have a higher rate of leaf senescence than non-floral tillers (Bittman et al., 1988) and are rejected by grazing cattle. Floral tillers of Stipa viridula Trin. and Pascopyrum sinithii (Rydb.) Love had 2 and 4 % units lower protein content and digesti­bility, respectively, than non-floral tillers (White, 1983). In the Parkland region of the prairies, smooth bromegrass (Bromus inermis Leyss.) is the most commonly seeded pasture grass. Cultivars of smooth bromegrass have been selected for high seed yielding potential to ensure their commercial success. The smooth bromegrass cultivars that produce seed prolifically in Canada (Carlton, Magna, and Signal) are derived from the northern ecotype while the less prolific varieties are derived from the southern ecotype. In the seed growing areas of the Canadian Prairies, southern cultivars yielded about 47 % less seed than northern cultivars (Knowles, 1984). The objective of this study was to compare the proportion of floral tillers and leaves and the nutritional quality of northern (prolific) and southern (non-prolific) cultivars of smooth bromegrass.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.385
Threshold uncertainty score0.883

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.232
Teacher spread0.218 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueUKnowledge (University of Kentucky)Same topicTurfgrass Adaptation and ManagementFrench-language works237,207