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Record W2161408684 · doi:10.1098/rsbl.2011.0950

Reversal of height dimorphism promotes pollen and seed dispersal in a wind-pollinated dioecious plant

2011· article· en· W2161408684 on OpenAlexaff
Melinda Pickup, Spencer C. H. Barrett

Bibliographic record

VenueBiology Letters · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBiologySexual dimorphismDioecyPollenBiological dispersalReproductionSeed dispersalPollinationAnemophilySeed dispersal syndromeSexual reproductionEcologyBotanyZoologyPollinatorPopulation

Abstract

fetched live from OpenAlex

Variation in the timing of reproductive functions in dioecious organisms may result in adaptive changes in the direction of sexual dimorphism during the breeding season. For plants in which both pollen and seeds are wind-dispersed, it may be advantageous for male plants to be taller when pollen is dispersed and female plants to be taller when seeds are dispersed. We examined the dynamics of height dimorphism in Rumex hastatulus, an annual, wind-pollinated, dioecious plant from the southern USA. A field survey of seven populations indicated that females were significantly taller than males at seed maturity. However, a glasshouse experiment revealed a more complex pattern of height growth during the life cycle. No dimorphism was evident prior to reproduction for six of seven populations, but at flowering, males were significantly taller than females in all populations. This pattern was reversed at reproductive maturity, consistent with field observations. Males flowered later than females and the degree of height dimorphism was greater in populations with a later onset of male flowering. We discuss the potential adaptive significance of temporal changes in height dimorphism for pollen and seed dispersal, and how this may be optimized for the contrasting reproductive functions of the sexes.

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 score0.531
Threshold uncertainty score0.190

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 head0.038
GPT teacher head0.186
Teacher spread0.147 · 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

Citations47
Published2011
Admission routes1
Has abstractyes

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