Beyond pollination syndromes? Reflections on the classifications of Federico Delpino
Bibliographic record
Abstract
BACKGROUND: Pollination syndromes are typically defined as idealized pollination modes based on a canonical set of traits. As such, they are often criticized as typological rather than empirical. These syndromes are attributed to the great Italian botanist Federico Delpino, who has borne some blame for their perceived shortcomings. Yet Delpino's original contribution, although it inspired the concept of pollination syndromes, differed significantly from it. What he proposed was, in contrast, an empirical classification of plants into floral functional types (FFTs), with pollination vector as the key trait. PERSPECTIVE: Delpino produced two functional type classifications. The first was published, approvingly, by H. Müller in 1873. This was the classification that, in the 20th century, evolved into the pollination syndrome concept. Delpino later proposed a more ambitious and innovative classification of animal pollination into 47 functional types in his monumental work, the Ulteriori Osservazioni. Müller strongly criticized this classification, but if Delpino's second scheme had been refined rather than dismissed, it might have shaped later developments in pollination biology in a highly beneficial way. As Löw wrote in 1895, it was 'one of the most ingenious and grand attempts' at a fundamentally open-ended problem. Müller proposed his own classification in 1881, but after that, interest in floral functional types in pollination ecology languished, eventually to be replaced by a different concept, that of pollination syndromes. In contrast, plant functional type (PFT) classification has become central in vegetation and global change ecology. CONCLUSION: Pollination ecology could usefully reflect on the PFT approach, even revisiting Delpino's work (which has never been translated from the original Italian) for inspiration. For some studies, an FFT approach, as pioneered by Delpino, could usefully replace a pollination syndrome-based approach.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.029 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.004 |
| 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".