The distributions of insect, wind and self pollination of plants in the Netherlands in relation to habitat types and 3D vegetation structure
Bibliographic record
Abstract
Plants can be pollinated in many ways, with insect, wind and selfing as the most common modes. While it seems likely that the occurrence of pollination modes is correlated with environmental conditions, e.g. vegetation structure, and this remains uncertain. Here, we mapped the composition of pollination modes of different plant groups (woody species, herbs, and grasses) across (semi-)natural habitats and their distributions in relation to 3D vegetation structure in the Netherlands. We found insect pollination is the most common mode across (semi-)natural habitats for woody species and herbs. Woody species pollinated by insects showed an even higher percentage in dune, river swamp and swamp peat than in other habitat types, whereas herbs showed a higher percentage of insect pollination in dune than in other habitat types. Grasses were always pollinated by wind or wind-self in all habitats. Woody plants pollinated by wind showed a positive relationship with canopy densities in three different strata from 2 to 20 m vegetation, while insect pollination showed a positive relationship with the canopy density of 0.5 to 2 m vegetation. All grass presented negative relationships with canopy density. Herbs showed different relationships with canopy densities of different strata dependent on pollination modes. Insect-pollinated species increased with canopy densities of low strata but decreased with canopy density of high strata, whereas wind-pollinated species decreased with canopy density of both low and high strata. We conclude that habitat and vegetation structure are important factors driving the distribution of pollination modes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".