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Record W2378232287

Optimizing the Accelerated Ageing Condition of Siberian wildrye Seeds

2013· article· en· W2378232287 on OpenAlexvenueno aff
Mao Pei-sheng

Bibliographic record

VenueSeed · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSeed Germination and Physiology
Canadian institutionsnot available
Fundersnot available
KeywordsGerminationAgeingDormancyHorticultureAccelerated agingElymusBiologySignificant differenceAgronomyChemistryMathematicsPoaceae
DOInot available

Abstract

fetched live from OpenAlex

Four samples of Siberian wildrye(Elymus sibiricus Linn.) seeds with different germination level were used in the experiment and conducted to analyze the effect of the ageing temperature(39,41,43 and 45 ℃) and ageing time(1,2,3,4,5,6 and 7 d) on the germination percentage,and to find out the suitable condition and procedure for the accelerated ageing test of Siberian wildrye seeds.The result indicated that the seed germination of Lot 1,Lot 2 and Lot 3 decreased,and the germination decrease had significant difference(p 0.05) at 39 ℃,41 ℃,43 ℃ and 45 ℃,but only at 45 ℃ the aging germination percentage of Lot 3 fell lowest.The aging germination percentage of Lot 4 seed sample was low,and there was significant difference(p 0.05) between 45 ℃ and the other treatments.The seed germination of Lot 1 and Lot 2 gradually decreased with the treatments of the ageing time from 1 d to 7 d,and the germination after ageing 1 d and 2 d were significant different(p 0.05) with others.Germination of Lot 3 and Lot 4 had peak value after ageing 4 d,and was significantly(p 0.05) different from ageing 2,3,5,6 and 7 d.The interaction between the ageing temperature and ageing time was connected with the standard germination percentage and dormancy of seed,and significantly(p 0.01) related with the germination rate after ageing of Siberian wildrye seeds.The result indicated that ageing temperature 45 ℃ was optimum temperature and ageing time 2 d was optimum time for accelerated ageing test of Siberian wildrye seeds.

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 categoriesInsufficient payload (model declined to judge)
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 score0.978
Threshold uncertainty score1.000

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 head0.023
GPT teacher head0.238
Teacher spread0.214 · 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.

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

Citations1
Published2013
Admission routes1
Has abstractyes

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