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Record W2848244919 · doi:10.1139/cjb-2018-0021

Incidence of blossom-end rot in elongated tomato fruit

2018· article· en· W2848244919 on OpenAlexvenueno aff
Lucas Baiochi Riboldi, Sabrina Helena da Cruz Araújo, Sérgio Tonetto de Freitas, Paulo R. C. Castro

Bibliographic record

VenueBotany · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBanana Cultivation and Research
Canadian institutionsnot available
Fundersnot available
KeywordsXylemBiologyHorticultureCultivarCropTranspirationBotanyAgronomyPhotosynthesis

Abstract

fetched live from OpenAlex

Blossom-end rot (BER) is a physiological disorder that can affect 100% of the fruit crop depending on the genotype. Tomato varieties with elongated fruit usually have a greater susceptibility to BER than other varieties. To evaluate and identify the possible physiological and morphological characteristics related to the onset of BER development, four varieties of long-shape tomato fruit with different susceptibility to BER: ‘San Marzano,’ ‘Banana Legs,’ ‘Roma,’ and ‘Mini-Roma’ were examined. Our results show that ‘San Marzano’ and ‘Banana Legs’ (elongated fruit) had a higher incidence of BER and lower Ca 2+ concentration in the distal fruit tissue. ‘San Marzano’ (the most elongated fruit) presented higher electrolyte leakage in the distal fruit tissue. By comparison, ‘Roma’ and ‘Mini-Roma’ (less elongated fruit) were less susceptible to BER and had a higher ratio for proximal/distal fruit Ca 2+ and a lower distal cell-wall bound content of Ca 2+ . Additionally, xylem functionality (vessels transporting water and solutes) in the distal fruit tissue was also higher in these more-tolerant varieties. These results support the theory that total fruit content of Ca 2+ is not the only factor determining fruit susceptibility to BER, but rather a balance between physiological and morphological factors that influence Ca 2+ transport and allocation in the fruit.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.909
Threshold uncertainty score0.999

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)0.0020.000

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 head0.032
GPT teacher head0.277
Teacher spread0.245 · 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 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

Citations14
Published2018
Admission routes1
Has abstractyes

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