Typology in pollination biology: Lessons from an historical critique
Bibliographic record
Abstract
Typological schemes that describe putative floral adaptations for pollinators have played a central role in pollination biology. In 1882 the prominent German botanist and Darwinist Hermann Müller commented critically on a precursor of modern versions of such “pollination syndromes” that had been developed by his Italian colleague Federico Delpino. Delpino also was a self-proclaimed Darwinist, but in fact adhered to teleology—explanation beyond nature. As a consequence he viewed his typology as reflecting a deeper ideal and thus as rigidly true, and rejected as unimportant any visitors to flowers that it did not predict. Although Müller also classified flowers as to pollinators, he considered such schemes to be fallible, and pondered what diversity and variation in floral visitors might mean. Müller’s comments, which we translate here, are of interest given that appeals to teleology have resurfaced from time to time in discussions of pollination syndromes, and more importantly because his warning against taking typological schemes too literally remains valid. Typology is a useful tool in biology, including pollination biology, but care must be taken that it does not replace nature as perceived reality.
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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 | 0.022 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.007 | 0.084 |
| Scholarly communication | 0.010 | 0.023 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.003 | 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".