Characterisation of seed marking types in chickpea (<i>Cicer arietinum</i> L.): Tiger stripe and other blemishes
Bibliographic record
Abstract
Abstract Desi chickpea is a significant export crop for Australia; Australia being the largest exporter globally. Visual appearance of the seed is an economically significant measure of seed quality by the Indian subcontinent, the major importer of desi chickpea worldwide. Any visual blemish on the seed is considered undesirable, regardless of the cause (biotic or otherwise). Literature on biotic causes of seed blemishes, such as ascochyta blight, are available; however, little could be found on abiotic blemishes. Abiotic seed blemishes caused by physiological plant responses are more commonly known as seed markings. Despite the presence of seed markings being confirmed by several chickpea‐producing countries during personal discussions (India, Canada), no scientific literature has been published. The aim of this study was to proactively seek out and characterise different types of seed marking patterns using a wide genetic pool of desi chickpea across a range of environments in Australia. Thirteen different seed marking patterns were identified in desi chickpea and three in kabuli chickpea, including several rare seed markings that were discovered, photographed, and described. Seed markings (blemishes thought to be caused by physiological plant stress) can be characterised as dark patterns on the testa (seed coat) that do not visually affect the underlying cotyledon. In contrast, other seed blemishes (caused by pests and disease, physical damage, or poor storage) were more likely to affect the cotyledons underlying the testa, but not always. This paper classifies and describes various types of seed markings and blemishes for future reference by the global chickpea industry.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".