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Record W1595267509 · doi:10.26786/1920-7603(2015)8

Pollinator dependency, pollen limitation and pollinator visitation rates to six vegetable crops in southern India

2015· article· en· W1595267509 on OpenAlexvenueno aff
Priya Davidar, Saranya Arwen Carr

Bibliographic record

VenueJournal of Pollination Ecology · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsnot available
Fundersnot available
KeywordsPollinatorPollinationBiologyPollenPlant reproductive morphologyCropOpen pollinationBotanyAgronomyHorticulture

Abstract

fetched live from OpenAlex

We investigated levels of pollinator dependency and pollinator visitation rates to flowers of six vegetable crops: brinjal (aubergine), tomato, chilli pepper (Solanaceae), okra (Malvaceae), bitter and snake gourds (Cucurbitaceae) in six small family farms in the Coimbatore region of southern India. We tested the null hypothesis that fruit set in these crops would be independent of pollinators. We assessed fruit set through self and cross pollination by pollen augmentation, by pollinator exclusion and open pollination. We evaluated pollen limitation by comparing percentage fruit set by hand outcrossed pollen with open pollination; pollinator dependency by differences in percentage fruit set by open pollination and autogamous pollination; and visitation rates to flowers by pollinating insects. Tomato, chilli and okra produced self-compatible hermaphrodite flowers, with higher levels of autogamous fruit set (32-76%) and significantly lower levels of pollinator dependency (0-37%), whereas andro-monoecious brinjal and monoecious gourds had significantly lower levels of fruit set through autogamy, and higher levels of pollinator dependency. Pollen limitation was not evident in any crop. Diverse pollinating insects visited the flowers, and the frequency of visits by different pollinator taxa differed with crop type. Native vegetation and uncultivated land may enhance pollinator diversity in small farms.

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)0.001
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 score0.043
Threshold uncertainty score0.308

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.040
GPT teacher head0.251
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

Citations13
Published2015
Admission routes1
Has abstractyes

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