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Record W2007832805 · doi:10.1002/jpln.200625209

Effect of calcium on cyclamen pedicel elongation

2007· article· en· W2007832805 on OpenAlexaboutno aff
Shaohong Tang, Weiyong Shi, Hailong Wang, Ancheng Luo

Bibliographic record

VenueJournal of Plant Nutrition and Soil Science · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Physiology and Cultivation Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPedicelCalciumBiologyHorticultureBiophysicsBotanyChemistry

Abstract

fetched live from OpenAlex

Abstract A greenhouse experiment was conducted to study the effect of calcium (Ca) supply on pedicel growth of Cyclamen persicum “Metis” during flower development. When flower buds started to develop, four foliar spray Ca treatments were applied, including Ca 0 (no added Ca as control), Ca(NO 3 ) 2 , CaEDTA, and CaEDTA+H 3 BO 3 . Calcium spray treatments significantly reduced the number of short pedicels. There were 62% of the flowers with short pedicels ( i.e. , flower height lower than the foliar height) in the control treatment (Ca 0 ). Short pedicels accounted for 23%, 12%, and 10% in the Ca(NO 3 ) 2 ‐, CaEDTA‐, and CaEDTA+H 3 BO 3 ‐treated plants, respectively. CaEDTA was a more effective Ca source than Ca(NO 3 ) 2 for improving cyclamen pedicel elongation and increasing flower numbers. Additional boron (B) supply may have enhanced Ca translocation within the plant. Transmission electron microscopy (TEM) indicated that pedicel‐cell structure was severely damaged in the control plants. Pedicel‐cell damage was greatly alleviated in plants treated with Ca(NO 3 ) 2 . No symptoms of cell damage were found in the CaEDTA‐ and CaEDTA+H 3 BO 3 ‐treated plants. Energy‐dispersive X‐ray spectrometer (EDX) analysis confirmed that CaEDTA+H 3 BO 3 and CaEDTA had increased the Ca : O ratios. Therefore, Ca localization in the pedicel cell had a great influence on the pedicel height of cyclamen, and Ca concentration could be a good indicator for undisturbed cyclamen flower development.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.700
Threshold uncertainty score0.139

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.018
GPT teacher head0.265
Teacher spread0.247 · 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

Citations1
Published2007
Admission routes1
Has abstractyes

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