Morpho-Agronomic and Biochemical Characterization of Accessions of Tiger Nut (Cyperus esculentus) Grown in the North Temperate Zone of China
Bibliographic record
Abstract
L.) has recently attracted increasing interest from scientific and technological communities because of its potential for serving as additional source of food, oil, and feed. The present study reports morphology and biochemical characterization of 42 tiger nut accessions collected from China and other counties performed in the 2020 and 2021 growing seasons at Nongan, Jilin Province. Assessment of variability of 14 agronomic traits including plant height, maturation, leaf width, tilling number, color, size, and shape: 100-tuber weight showed a wide range of phenotypic variation. The color, size, and shape and maturation of the tubers, as well as the leaf width, were the most distinct characteristics describing variation among the accessions. Compositional analyses of major nutritional components of the tubers reveals that this crop could be a source of high-value proteins, fatty acids, and carbohydrates. Specifically, tiger nut tubers contained high levels of starch, oil, and sugars, and significant amounts of fiber, Ca, P, and Na. Furthermore, the tubers appeared to be a good source of proteins as they contain 16 amino acids, including the essential ones. Amino acid profiles were dominated by aspartic acid followed by glutamic acid, leucine, alanine, and arginine. Overall, these results demonstrated that tiger nut is well adapted to the temperature and light conditions in the north temperate zone of China, even with a shorter growth season. The tiger nut accessions collected here exhibited wide variations for agronomical and biochemical traits, suggesting potential for potential for breeding improvement by maximizing the fresh tuber and grass yield based on the optimal selection of genetic characteristics in climate and soil conditions of northern China.
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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) | 0.000 | 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 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".