Automated starch granule classification using high-throughput microscopy and machine learning
Bibliographic record
Abstract
Starch analysis is widely used in archaeology to investigate the processing of wild plants for food and medicine, as well as the domestication and spread of cultigens. Starch analyses are dependent on the development and use of identification keys. To date, all published methods to identify starch granules are either time-consuming to produce and apply, or difficult to statistically validate for their accuracy. The method outlined in this paper mitigates two major production bottlenecks experienced by current methods. First, the necessary task of collecting reference images of starch granules is accelerated using a multispectral imaging flow cytometry (MIFC), a high throughput microscope that collects thousands of images per second. Second, the traditional step of collecting measurements of individual granules by hand is eliminated. Processed image sets are used directly to train image recognition (machine learning) algorithms for species identification. This method produces identification accuracies that are comparable to, or better than, other published methods. When this method is applied using 15,000 images of starch granules from 15 plants occurring in North America, validation accuracies were observed to be as high as 100 %. This method promises to provide a feasible, cost-effective, and accurate means to identify starch granules recovered from archaeological materials. • Deep learning algorithms can be used to classify images of modern starch granules with a high degree of accuracy. • Multispectral imaging flow cytometry can be used to collect image datasets large enough to train deep learning algorithms. • This methodology promises a feasible alternative to other published statistical starch grain classification methods.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".