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Record W2595724351 · doi:10.26786/1920-7603(2017)2

Honey bees ensure the pollination of <i>Parkia biglobosa</i> in absence of bats

2017· article· en· W2595724351 on OpenAlexvenueno aff
Kristin Marie Lassen, Moussa Ouédraogo, Yoko Luise Dupont, Erik Dahl Kjær, Lene Rostgaard Nielsen

Bibliographic record

VenueJournal of Pollination Ecology · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsnot available
Fundersnot available
KeywordsParkia biglobosaPollinatorBiologyPollinationPollenHoney beeBotanyEcologyLocust

Abstract

fetched live from OpenAlex

One of the socio-economically important West African parkland trees, Parkia biglobosa yields pods consumed by people and animals. Being animal-pollinated, it is unknown how climate change will affect the species if the guild of pollinators changes. Here, we compare the pollinator guilds and the pod production at two climatically different study sites: the first site, Tiba was drier with lower tree density and fewer fruit bats, whereas the second site, Pinyiri was more humid, with higher tree density and more fruit bats. We carried out a pollinator exclusion trial with bags separating the flower-visitors and made observations of the flower-visitors. Furthermore, we calculated pollination distances based on paternity analysis of the seeds produced within the trial. The numbers of immature fruits were similar for the sites, but the drier site, Tiba experienced more abortion and decreased pod set. At both sites, exclusion of bats led to marginally reduced pod set, while exclusion of bats and honey bees led to significantly reduced pod set. We found a small effect of stingless bees and solitary bees but only at the more humid site, Pinyiri. Tiba experienced a higher level of self-pollination, fewer pollen donors per tree, and longer median distances of pollen flow, compared with Pinyiri. Implications of the results for management of the species and its pollinators in the face of climate change are discussed.

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)0.001
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 score0.809
Threshold uncertainty score0.390

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.032
GPT teacher head0.246
Teacher spread0.214 · 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

Citations6
Published2017
Admission routes1
Has abstractyes

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