Image Analysis Protocol for Detecting and Counting Viable and Inviable Pollen Grains
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Estimating total pollen number and viability is labor intensive and time consuming. Nevertheless the information is crucial for a range of plant biologists from plant breeders to evolutionary biologists. Viability is determined by dye staining and counting colored (viable) and transparent (inviable) grains under a compound microscope. Existing protocols have been standardized to speed up total pollen counts but their success in determining viability is rather limited because they do not incorporate staining techniques. Some of the published protocols that determine viability do so based on statistical methods but focus on just one parameter such as pollen diameter requiring manual standardization to generate two distinct size distributions for viable and inviable pollen. We demonstrate a digital image processing protocol that can count viable and inviable pollen by distinguishing colored and transparent objects in the image space, saving valuable labor and time. Using pollen grains from two plants, Collinsia heterophylla and Brassica napus, we show that differences between viable and inviable pollen are best described by a complex measure, the grain shape. By measuring several parameters such as area, length, width, circularity and elongation, while retaining all the advantages of traditional staining process, our procedure increases the accuracy of viability estimates. The only drawback of our protocol is that it uses the NIS elements a software specific to Nikon instruments.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it