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Record W1984003700 · doi:10.2135/cropsci2010.05.0262

Long Juvenile Soybean Flowering Responses under Very Short Photoperiods

2010· article· en· W1984003700 on OpenAlexaff
Elroy R. Cober

Bibliographic record

VenueCrop Science · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoybean genetics and cultivation
Canadian institutionsAgriculture and Agri-Food Canada
FundersU.S. Department of Agriculture
KeywordsBiologyphotoperiodismBackcrossingJuvenileIntrogressionPhenologyBotanyHorticultureGeneticsGene

Abstract

fetched live from OpenAlex

Long juvenile (LJ) soybean [ Glycine max (L.) Merr.] lines exhibit delayed flowering under short photoperiods. Two loci, J and E6 , have been reported to control this response. The objectives of this work were to compare phenology of E6E6 and e6e6 isolines across a range of photoperiods, to determine the genetic control of the LJ trait during the introgression of e6 into an early maturity background, and to compare e6e6 and jj lines in short photoperiods. Flowering time of ‘Paraná’ ( E6E6 ) and its LJ isoline ‘Paranagoiana’ ( e6e6 ) was observed in photoperiods of 3 to 16 h. Both lines flowered late (about 100 d) in a 16 h photoperiod. Paraná flowered in less than 30 d in 6, 8, 10, and 12 h photoperiods. Paranagoiana responded to decreases in photoperiod from 12 to 4 h with earlier flowering, although always flowered a minimum of 5 d later than Paraná. The LJ trait in Paranagoiana was introgressed into OT94‐47 with selection for late flowering under 12 h photoperiods. From the initial cross to the third backcross, F 2 populations exhibited a 15:1 early:late flowering ratio in 12 h photoperiods. A similar 15:1 ratio was observed in a cross between OT94‐47 and LJ PI 159925. In comparisons, under 3 to 12 h photoperiods, Paranagoiana and PI 159925 flowered similarly and later than Paraná. It appears that the LJ lines may have a 5 d juvenile period but flowering delays greater than 5 d are likely due to photoperiod responses to very short photoperiods (≥5 h).

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.698
Threshold uncertainty score0.471

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 studies0.0010.000
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 head0.025
GPT teacher head0.259
Teacher spread0.233 · 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

Citations39
Published2010
Admission routes1
Has abstractyes

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