Notes on pollination ecology of Altensteinia fimbriata Kunth in the city of Quito, Ecuador
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".