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Record W4361191026 · doi:10.1101/pdb.top107666

Mosquito Pollination and Sugar Detection Methods: An Overview

2023· article· en· W4361191026 on OpenAlexaff
Daniel A.H. Peach

Bibliographic record

VenueCold Spring Harbor Protocols · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSugarNectarPollinationBiologyPollinatorPollenEcologyFood science

Abstract

fetched live from OpenAlex

The interactions between mosquitoes and plants and, particularly, between mosquitoes and plant sugars from flowers and other structures are often overlooked and are vastly underresearched compared to mosquito-vertebrate or mosquito-pathogen interactions. Given the importance of mosquito nectar-feeding behavior, as well as its impact on vectorial capacity and its implications for vector suppression, a better understanding of mosquito-plant interactions is needed. Direct observation of mosquitoes visiting plants to obtain sugar and other nutrients can be difficult because females may leave flowers to seek a blood meal from the observer, but this can be overcome with the right experimental procedures. This article discusses methods for the detection of sugar in mosquitoes and for assessing mosquito pollination.

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)0.005
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0110.004
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0030.002
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0040.005

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 head0.175
GPT teacher head0.359
Teacher spread0.184 · 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 designNot applicable
Domainnot available
GenreMethods

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
Published2023
Admission routes1
Has abstractyes

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