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Record W2137668195 · doi:10.3732/ajb.93.6.910

Fruit production in cranberry (Ericaceae: <i>Vaccinium macrocarpon</i>): a bet‐hedging strategy to optimize reproductive effort

2006· article· en· W2137668195 on OpenAlexafffund
Adam O. Brown, Jeremy N. McNeil

Bibliographic record

VenueAmerican Journal of Botany · 2006
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsWestern UniversityUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Louisville
KeywordsBiologyPollinationPollinatorEricaceaeBotanyReproductive successVacciniumContext (archaeology)OvulePollenHand-pollinationHorticulturePopulation

Abstract

fetched live from OpenAlex

In the cultivated cranberry (Vaccinium macrocarpon), reproductive stems produce 1-3 fruit even though they usually have 5-7 flowers in the spring. We undertook experiments to test the hypothesis that this was an adaptive life history strategy associated with reproductive effort rather than simply the result of insufficient pollination. We compared fruit production on naturally pollinated plants with those that were either manually pollinated or that were caged to exclude insects. Clearly, insects are necessary for the effective pollination of cranberry plants, but hand pollination of all flowers did not result in an increase in fruit number. Most of the upper flowers, which had significantly fewer ovules than did the lower flowers, aborted naturally soon after pollination. However, when the lower flower buds were removed, the upper flowers produced fruit. This suggests that the upper flowers may serve as a backup if the earlier blooming lower ones are lost early in the season. Furthermore, the late-blooming flowers may still contribute to the plant's reproductive success as visiting pollinators remove the pollen, which could serve to sire fruit on other plants. These results are discussed in the context of their possible evolutionary and proximate causes.

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 score0.798
Threshold uncertainty score0.319

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.016
GPT teacher head0.220
Teacher spread0.203 · 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

Citations54
Published2006
Admission routes2
Has abstractyes

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