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Record W1494894257 · doi:10.1080/00103624.2015.1043458

Response of Gemlik Olive Trees to Soil and Foliar Treatments of Iron in Combination with Zinc and Boron

2015· article· en· W1494894257 on OpenAlexfundno aff
H. Başar, Serhat Gürel

Bibliographic record

VenueCommunications in Soil Science and Plant Analysis · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Micronutrient Interactions and Effects
Canadian institutionsnot available
FundersUniversity of Lethbridge
KeywordsBoraxZincSulfateBoronChemistryCultivarIron sulfateHorticultureField experimentBiologyRaw material

Abstract

fetched live from OpenAlex

This research was conducted to determine the effectiveness of various treatments in correction of single deficiency of iron (Fe) and multiple deficiencies of Fe, zinc (Zn), and boron (B) in an olive cultivar (Gemlik) in the southeastern Marmara region of Turkey. This study was consisted of four field experiments, which included control, soil and foliar applications of Fe alone, and combinations with Zn and B. Soil applications of the compounds were only performed in the first year of the experiments to estimate residual effect of soil applications in the following year. Foliar applications were sprayed onto leaves two and four times at two doses in consecutive years. Soil application of iron sulfate did not increase Fe concentrations in the both leaves and fruits. Foliar applications of iron sulfate considerably elevated leaf total and active Fe concentrations, but the effect of the foliar applications on fruit Fe concentrations was small. Two foliar applications of iron in each season seemed to be appropriate treatment in the all experiments, as well. To maintain sufficient Fe concentrations, especially in the newly developing tissues of olive trees, foliar application of Fe should be conducted at least four times at the lowest dose as performed in the experiments. Foliar applications of double and triple combinations of iron sulfate with zinc sulfate and borax increased significantly B and Zn concentrations in the trees, as well.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.094
Threshold uncertainty score0.979

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 studies0.0000.000
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 head0.033
GPT teacher head0.273
Teacher spread0.240 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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