Morphometrics of Starch Granules from Sub-Saharan Plants and the Taxonomic Identification of Ancient Starch
Bibliographic record
Abstract
The assumption that taxonomy can be ascertained by starch granule shape and size has persisted unchallenged since the late nineteenth and early twentieth century biochemistry. More recent work has established that granule morphological affinity is scattered throughout phylogenetic branches, morphotype proportions vary within the genus, granules from closely related genera can differ dramatically in shape, and size variations do not reflect phylogenetic relationships. This situation is confounded by polymorphism at the species and tissue level, resulting in redundancy and multiplicity.This paper classifies morphological features of starch granules from 77 species, 31 families, and 22 orders across three African ecoregions. This is the largest starch reference collection published to date, rendering the dataset uniquely well suited to explore i) the diagnostic power of unique morphometric classifiers and their frequency, ii) morphotypes that cut across taxonomic boundaries, and iii) issues surrounding the minimum counts needed to accurately reflect granule polymorphism, variability, and identification.In a collection of 23,100 granules, taxonomic identification occurred very rarely. In the instances it did, it was at the species level, with no occurrences of a single morphotype or complement identifying all species within a family or genus. Some families cannot be uniquely identified, and morphometric types are shared despite taxonomic distance for three quarters of the taxa. However, this reference collection boasts 98 unique identifiers located in the Arecaceae, Convolvulaceae, Cyperaceae, Dioscoreaceae, Fabaceae, Musaceae, Pedaliaceae, Poaceae, and Zamiaceae.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".