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Record W2997097823 · doi:10.26786/1920-7603(2019)547

Insect pollinators of conference pear (<i>Pyrus communis</i> L.) and their contribution to fruit quality

2019· article· en· W2997097823 on OpenAlexvenueno aff
Michelle T. Fountain, Zeus Mateos‐Fierro, Bethan Shaw, Phil Brain, Álvaro Delgado

Bibliographic record

VenueJournal of Pollination Ecology · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsnot available
FundersVlaamse regering
KeywordsPEARPollinationOrchardPyrus communisBiologyPollinatorParthenocarpyInsectHorticultureBotanyCultivarPollen

Abstract

fetched live from OpenAlex

The pear (Pyrus communis L.) cultivar, Conference, is parthenocarpic but misshapes and marketable fruit losses of 6% at harvest are common. In other studies, insect flower visitors are identified as important for apple quality, but far fewer studies have examined the effects of insects and cross-pollination on pear quality. Using a range of replicated field experiments, this project aimed to determine the; 1) biodiversity of pear blossom insect visitors, 2) pollen limitation and fruit quality as a function of distance from the orchard edge and number of insect visitors, and 3) importance of cross pollination on fruit quality. A wide range of insects, >30 species, visited pear flowers including honey bees, bumble bees, solitary bees and hoverflies. Honey bees were the most frequent visitors, but all guilds, to a greater or lesser extent, made contact with the reproductive parts of the flower. Insect visits resulted in ~10% higher fruit set. There was no effect of distance from the edge (up to 50 m) of orchard on the quality of pears, and no consistent difference in the guild of insects visiting at distances from the orchard boundary. Cross-pollination with the variety Concorde produced better quality Conference fruits. We discuss how pollination of Conference pears could be managed to improve yields of marketable fruit.

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 score0.892
Threshold uncertainty score0.193

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.041
GPT teacher head0.250
Teacher spread0.209 · 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

Citations14
Published2019
Admission routes1
Has abstractyes

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