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Record W2748061936 · doi:10.5539/sar.v6n4p55

Grower Perception of the Significance of Weaver Ants as a Fruit Fly Deterrent in Tanzanian Smallholder Mango Production

2017· article· en· W2748061936 on OpenAlexvenueno aff
Nina Kirkegaard, Theodosy Msogoya, Joachim Offenberg, B.W.W. Grout

Bibliographic record

VenueSustainable Agriculture Research · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect behavior and control techniques
Canadian institutionsnot available
FundersDanish International Development AgencyUdenrigsministeriet
KeywordsTanzaniaIndigenousRipeningBiologyAgroforestryInfestationHorticultureToxicologyYield (engineering)GeographyEcology

Abstract

fetched live from OpenAlex

Managed populations of weaver ants in mango trees have been used successfully in Australia, SE Asia and parts of Western Africa to deter fruit flies from ovipositing in ripening fruits. The presence of indigenous weaver ants in mango trees of smallholder growers in Tanzania offers the possibility of exploiting them as an affordable, environmentally -friendly method to improve marketable fruit yield and quality. In a preliminary interview study in a mango-growing region of rural Tanzania, the farmers were not convinced of any beneficial, deterrent effect attributable to the indigenous weaver ants in their trees and were sceptical of any likely value as a biological control technique. Additionally, fruit fly infestation was not seen as a priority problem and subsequent enquiry and investigation showed that, fortuitously, traditional, local practices for storage and enhancing ripening prevented the development of a significant proportion of any deposited eggs. Subsequent field studies supported the grower perceptions as they recorded only an erratic and limited deterrent effect.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.015
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.040
GPT teacher head0.319
Teacher spread0.279 · 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 source (direct Gemma or distilled Codex), 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
Published2017
Admission routes1
Has abstractyes

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