A standardized and efficient technique to estimate seed traits in plants with numerous small propagules
Bibliographic record
Abstract
Abstract Premise Variation in seed traits is common within and among populations of plant species and often has ecological and evolutionary implications. However, due to the time‐consuming nature of manual seed measurements and the level of variability in imaging techniques, quantifying and interpreting the extent of seed variation can be challenging. Methods We developed a standardized high‐throughput technique to measure seed number, as well as individual seed area and color, using a derived empirical scale to constrain area in Arabidopsis thaliana, Brassica rapa , and Mimulus guttatus . We develop a specific rational model using seed area measured at various spatial scales relative to the pixel count, observing the asymptotic value of the seed area as the modeled number of pixels approaches infinity. Results We found that our model has high reliability in estimating seed traits and efficiently processes large numbers of images, facilitating the quantification of seed traits in studies with large sample sizes. Discussion This technique facilitates consistency between imaging sessions and standardizes the measurement of seed traits. These novel advances allow researchers to directly and reliably measure seed traits, which will enable tests of the ecological and evolutionary causes of their variation.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".