Bibliographic record
Abstract
Pollination studies in East Asia have been developing rapidly in recent decades. East Asia may provide important information on many aspects of plant-pollinator interactions because of the rich fauna and flora and highly heterogeneous environments that occur there. In this review, plant-pollinator interactions in East Asia were summarized. Bumblebees are important pollinators of many plant species in East Asia, as well as in Europe and North America. Native honeybees may also have important roles in pollination in East Asia. Bird pollination and hawkmoth pollination may be less common in East Asia than in North America. Geographic variation in pollination interactions is expected because several types of pollinators are rare or absent in some habitats or geographic regions. For example, specialized nectar-feeding vertebrates like sunbirds and pteropodid bats are absent from most of East Asia except for some areas in its southern part. Opportunistic nectar-feeding vertebrates may have important roles in pollination where specialized nectar-feeding vertebrates are absent. Human impacts on plant pollinator interactions are understudied in this region. However, climate change, habitat degradation, and invasive species may have negative impacts on plant-pollinator interactions and thus plant reproductive success there. The information available on the plant-pollinator interactions in East Asia is still limited because many plant and pollinator taxa and many types of habitats are understudied.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".