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Record W2039943034 · doi:10.5539/jas.v5n6p190

Sunki Mandarin vs Poncirus trifoliata Hybrids as Rootstocks for Pera Sweet Orange

2013· article· en· W2039943034 on OpenAlexvenueno aff
Evandro Henrique Schinor, Mariângela Cristofani–Yaly, Marinês Bastianel, Marcos Antônio Machado

Bibliographic record

VenueJournal of Agricultural Science · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Physiology and Cultivation Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPeraRootstockOrange (colour)HybridCitrus × sinensisCanopyHorticultureBiologyAbiotic componentBotanyTrifoliate orangeMandarin ChineseEcology

Abstract

fetched live from OpenAlex

Obtaining new rootstocks that have resistance to biotic and abiotic factors is one of the main goals of breeding programs for citrus. This study evaluated the performance of 42 hybrids of Sunki mandarin (Citrus sunki Hort. ex Tanaka) vs. Poncirus trifoliata cv. Rubidoux, as rootstock for Pera sweet orange. The experiment was conducted in Colômbia, São Paulo, Brazil, in randomized blocks with three replications in a spacing of 6.0 m x 3.5 m. The trees were seven years old and the experiment was conducted without irrigation. We quantified the variables height, diameter and canopy volume, and production of plants, besides the physico-chemical analysis of fruits. After the natural period of drought in the region, the trees were evaluated for resistance to drought, with scale ranging from 1 to 3. Plants were also evaluated for symptoms of citrus sudden death and compatibility canopy/rootstock. Differences were observed in height, diameter and canopy volume of Pera sweet orange grafted on 42 hybrids of Sunki mandarin x Poncirus trifoliata (TSxPT). Differences were observed in the physico-chemical characteristics of fruits of Pera sweet orange grafted on different hybrids TSxPT. Different degrees of drought tolerance were observed and six hybrids were resistant. The hybrids TSxPT 245 and 254 showed incompatibility with variety of Pera sweet orange.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.974
Threshold uncertainty score0.576

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.020
GPT teacher head0.241
Teacher spread0.222 · 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 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

Citations36
Published2013
Admission routes1
Has abstractyes

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