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Record W1554436871 · doi:10.26786/1920-7603(2012)14

Estimating pollination success with novel artificial flowers: Effects of nectar concentration

2012· article· en· W1554436871 on OpenAlexaffvenue
James D. Thomson, Jane E. Ogilvie, Takashi Makino, Angela Arisz, Sneha Raju, Vanessa Rojas-Luengas, Marcus G. Tan

Bibliographic record

VenueJournal of Pollination Ecology · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNectarPollenBiologyPollinationSugarReceiptPollen sourceZoophilyBotanyReproductive successHorticulturePollinatorPopulationFood scienceDemography

Abstract

fetched live from OpenAlex

We developed novel artificial flowers that dispense and receive powdered food dyes as pollen analogues while their nectar is replenished by capillary action. Dye receipt, which can be measured colourimetrically, is a direct surrogate for pollen receipt or female reproductive success, but can also serve to compare pollen donation (male reproductive success) from flowers with different colours of dye. By allowing captive bumble bee colonies to visit large arrays of such flowers, we investigated whether total dye receipt depended on the sugar concentration of a flower’s nectar. Estimating pollen transfer, rather than simply visitation rate, is appropriate for this question because flowers with more concentrated nectar might accrue more pollen not only through higher visitation rates but also through longer visits that transfer more pollen per visit. Flowers with richer nectar did receive more dye regardless of their spatial arrangement, but the effect was greatest when rich and poor flowers were segregated in large blocks, as opposed to being intermingled.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.751
Threshold uncertainty score0.123

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 head0.236
Teacher spread0.212 · 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

Citations19
Published2012
Admission routes2
Has abstractyes

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