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Record W2052837384 · doi:10.5539/jps.v3n1p35

Reproductive Strategy of Aegiceras corniculatum L. (Blanco.) - A Mangrove Species, in MNP&S, Gujarat, India

2013· article· en· W2052837384 on OpenAlexvenueno aff
Richa Pandey, C. N. Pandey

Bibliographic record

VenueJournal of Plant Studies · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal wetland ecosystem dynamics
Canadian institutionsnot available
FundersMinistry of Environment
KeywordsPollinatorMangroveBiologyNectarPollinationEcologyReproductive successInflorescenceBotanyPollen

Abstract

fetched live from OpenAlex

Mangroves occur in the tropical and subtropical inter-tidal regions of the world. Owing to their locations, they are expected to have a reproductive strategy which can facilitate generalized pollination. The present work has examined the floral biology, breeding system, pollinator resource and their efficiency, and the reproductive strategy of Aegiceras corniculatum L. (Blanco) at three islands (three populations) in Marine National Park and Sanstuary (MNP&S), Gulf of Kachchh, Gujarat, India. The temporal relations in the floral processes such as anther dehiscence, stigma receptivity and nectar secretion were studied and the results were juxtaposed to have comprehensive view. The floral life is very long (21 days) and the pace of floral transformation varies with the floral process. Significant diurnal variations in the stigma receptivity and nectar secretions influence the pollinators’ availability during different periods of the day. All the three breeding systems are present indicating occurrence of autogamy. However, strong protandry reduces its possibility to a significant level. Further, the asynchrony in the flowering processes at inflorescence level and the foraging behavior of pollinators increase the possibility of geitonogamy. The reproductive strategy of the plant is inclined towards cross pollination with keeping some possibility of self-pollination.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.129
Threshold uncertainty score0.478

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.025
GPT teacher head0.241
Teacher spread0.216 · 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

Citations4
Published2013
Admission routes1
Has abstractyes

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