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Record W4405548088 · doi:10.26786/1920-7603(2024)786

Notes on pollination ecology of Altensteinia fimbriata Kunth in the city of Quito, Ecuador

2024· article· en· W4405548088 on OpenAlexvenueno aff
Martín Carrera, Luis E. Baquero

Bibliographic record

VenueJournal of Pollination Ecology · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsnot available
Fundersnot available
KeywordsPollinatorPollinationBiologyEcologyOrchidaceaeNectarHabitat

Abstract

fetched live from OpenAlex

Most orchid species face significant challenges in urban environments. Particularly, pollination services and reproductive success can be altered due to habitat fragmentation related to human activities. Despite this, several terrestrial orchid species thrive in these environments. This study investigates the pollination ecology of Altensteinia fimbriata, a terrestrial orchid prevalent in disturbed habitats from the neotropics. This research aims to identify the pollination mechanism and to list the pollinators and floral visitors associated to A. fimbriata. During 30 hours of observation in a patch with 60 inflorescences of A. fimbriata within an anthropic area of the city of Quito, Ecuador, we recorded 121 visits from ten moth species, identifying four moth species as effective pollinators. Three of the pollinator species were noctuid moths. Moth activity peaked in the evening, coinciding with the emission of distinctive floral scents, suggesting that both scent and nectar attract visitors. We found that the four pollinators of A. fimbriata transfer pollinariums using their legs. This pollination mechanism is found in other terrestrial orchid species from different subtribes where noctuid moths are also the main pollinators. Our findings highlight the adaptability of A. fimbriata in urbanized areas and emphasize the need to understand its pollination dynamics in the context of global change.

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 score0.421
Threshold uncertainty score0.229

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 head0.049
GPT teacher head0.266
Teacher spread0.218 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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