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Record W4283386777 · doi:10.3390/agriculture12070915

Early Plant Development in Intermediate Wheatgrass

2022· article· en· W4283386777 on OpenAlexaff
Douglas J. Cattani, Sean R. Asselin

Bibliographic record

VenueAgriculture · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBioenergy crop production and management
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of Manitoba
Fundersnot available
KeywordsSeedlingBiologyTiller (botany)AgronomyOutcrossingHorticultureSpecific leaf areaCropBotany

Abstract

fetched live from OpenAlex

Early seedling developmental morphology influences plant growth and development and ultimately crop biomass and grain yields. We used six half-sibling plants of intermediate wheatgrass (IWG) (Thinopyrum intermedium, (Host), Barkworth and Dewey) to develop an obligate outcrossing species, to develop six maternal lines. Thousand seed weights (TSW) were consistently different amongst plants, averaging from 6.28 to 9.62 g over the three harvest years. Seedlings from the largest seed of each line were studied for early plant development under controlled conditions (22 °C/18 °C, 16/8 h day/night) with destructive harvests at 21, 28, 35 and 42 days after imbibition (DAI) through six grow-outs. Haun stage, and tiller umber and origin, were noted daily, and dry weight plant−1 (DWP) measured at the dates noted above. Leaf-blade length and width were measured in four grow-outs and leaf area estimated. Seedling development data showed some differences between lines and was similar in all lines studied. Data was combined to garner an understanding of early IWG development. Tillering began as the third leaf completed emergence. Coleoptile tillers and rhizomes were infrequent. DWP was best estimated using the main stem leaf area. A large-, a medium- and a small-seeded line were statistically identical for many characteristics including DWP indicating that TSW did not influence seedling vigor. The main stem leaf area may be used non-destructively to improve plant populations for early DWP selection.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.454
Threshold uncertainty score0.856

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 studies0.0000.000
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 head0.010
GPT teacher head0.169
Teacher spread0.159 · 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 designNot applicable
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

Citations5
Published2022
Admission routes1
Has abstractyes

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