Classification of trichome types within species of the water fern <i>Salvinia</i>, and ontogeny of the egg-beater trichomes
Bibliographic record
Abstract
Species of the water fern Salvinia are well known for their extremely water-repellent floating leaves. The architecture of Salvinia surfaces is of great interest for biomimetic applications, because submersed in water, they retain air films for a long period. Knowledge of these surfaces is also important for pest control, since one species ( Salvinia molesta D.S. Mitch.) is a pantropic invasive aquatic weed. The micromorphology of the leaf surfaces of six representative species has been characterized by scanning electron microscopy. Based on their morphology, the trichomes are classified in four types, named after the typical species. Among the species examined, numbers, distribution, and sizes of the trichome types vary significantly. The simplest types, the Cucullata trichomes, are multicellular, uniseriate, and up to 800 µm high. Groups of two multicellular, uniseriate trichomes are described as the Oblongifolia trichomes. The Natans trichomes are grouped as four multicellular, uniseriate trichomes. The Molesta trichomes are composed of four trichomes, which are connected by the second last apical cells of the trichome. The ontogeny of the Molesta trichome groups was puzzling and resulted in various names being applied to them (“Krönchenhaare,” “egg-beater,” or “coroniform” hairs). Their unique development from four solitary uniseriate trichomes to groups of four connected trichomes is described in detail.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".