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Record W3127412735 · doi:10.5109/4103696

Effects of Eco–Friendly Flower Thinning Formulations on a Pollination Insect, Apis mellifera

2020· article· en· W3127412735 on OpenAlexaff
Tae-Kwon Son, Hwal‐Su Hwang, Md Munir Mostafiz, Yukio Ozaki, Kyeong–Yeoll LEE

Bibliographic record

VenueJournal of the Faculty of Agriculture Kyushu University · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsNutrasource
FundersMinistry of Science and ICT, South KoreaKorea Industrial Technology Association
KeywordsPollinationInsectThinningBiologyEnvironmentally friendlyBotanyHorticultureEcologyPollen

Abstract

fetched live from OpenAlex

Flower thinning is necessary for crop production in various orchards. The effects of various ingredients of Koduri–Plus, an eco–friendly flower thinning formulation (FTF), were determined on the major pollination insect Apis mellifera. Three different FTFs, a mixture of 0.7% zinc and 1.5% manganese (A), a mixture of 0.7% zinc and 2.0% boron (B), a mixture of 2.0% seaweed extract (C), and lime sulfur solution were examined by measuring the contact and oral toxicities against adult worker bees. Both direct spray and oral ingestion of all three 1% FTF solutions did not cause any lethal effects for workers based on 72 h observation, but treatment with 1% lime sulfur solution increased worker mortality. Oral ingestion of FTF A and FTF C did not inhibit acetylcholinesterase (AChE) activity of workers at 24 h after treatment, but was slightly decreased by FTF B treatment. However, oral ingestion of the organophosphate pesticide dichlorvos or lime sulfur solutions significantly inhibited AChE activities. Our results suggest that manganese and seaweed extract of FTFs were not toxic for honeybees, in terms of contact and ingestion. Therefore, newly developed FTFs can be used to improve flower thinning activity without any detrimental effects on pollinating insects.

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 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 score0.854
Threshold uncertainty score0.168

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)0.0000.000

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 head0.024
GPT teacher head0.184
Teacher spread0.160 · 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
Published2020
Admission routes1
Has abstractyes

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