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Record W2963910061 · doi:10.1139/cjb-2019-0007

Induced cleistogamy: A strategy for reproductive assurance in <i>Murdannia nudiflora</i> (Commelinaceae)

2019· article· en· W2963910061 on OpenAlexvenueno aff
Viswanathan Veena, Santhosh Nampy

Bibliographic record

VenueBotany · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsnot available
FundersKerala State Council for Science, Technology and EnvironmentUniversity of Calicut
KeywordsBiologyPollinatorPollinationReproductive successBotanyReproductive isolationMatingAnthesisFecundityBiological dispersalEcologyPollenPopulation

Abstract

fetched live from OpenAlex

Murdannia nudiflora (L.) Brenan is a day flower that is dependent on entomophilous pollination. Despite the lack of pollinator attractants and its short flower longevity, M. nudiflora shows high rates of fecundity, which lead to their rapid dispersal. In addition to monitoring the vegetative reproductive methods and the effect of select environmental parameters (atmospheric temperature, relative humidity, and precipitation rates) in M. nudiflora, we evaluated mating systems in this species via artificial crosses. This species has evolved to undergo autogamy to ensure reproductive success when pollinators are insufficient; cleistogamous flowers are produced for optimal pollinator activity when environmental conditions are unfavorable. The number of cleistogamous flowers produced was found to be positively correlated with humidity and precipitation rates, and negatively correlated with temperature. In addition, it was found that high humidity and precipitation always hinder insect pollinators. Therefore, the combination of vegetative reproduction, entomophily, and cleistogamous self-pollination helps this weedy species to propagate with maximum efficiency. Murdannia nudiflora employs pre-anthesis cleistogamy as a mating strategy for reproductive assurance while retaining mechanisms that promote out-crossing.

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 score0.886
Threshold uncertainty score0.210

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 head0.065
GPT teacher head0.249
Teacher spread0.184 · 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

Citations10
Published2019
Admission routes1
Has abstractyes

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