Seasonal changes in protein, amino acid and elemental composition of perennial nodules of beach pea
Bibliographic record
Abstract
Changes in proteins, amino acids and elements were studied in the perennial nodules of beach pea during winter, summer and fall. Accumulation of total protein content in the nodules increased from mid-summer to early winter and then decreased. Among the total amino acids studied, arginine, cystathionine, ethanolamine, histidine, hydroxyproline, ornithine and proline were found to increase in winter nodules. γ-aminobutyric acid was found to be significantly higher in fall and summer, whereas sarcosine was higher in summer and winter. Large amounts of K followed by Ca were found in almost all nodule tissues. Phosphorus, A l, Si and Cu showed significant variation among different nodule tissues within winter and summer. In the nodular tissue, significantly larger amounts of Na, K and Mg were found in the winter and S in the summer. In both winter and summer, no significant difference could be observed in the distribution of Cl, Mo, Ca, Mn, Fe and Zn among nodule tissues. Irrespective of nodule tissues, the complete nodule showed the following seasonal changes: high K, Ca and Zn in winter; high Cl and Al in summer; high S and Si in fall; high Mn in both winter and summer; high Cu in both winter and fall; high Na, Mg and Fe in both summer and fall; no significant changes in the amounts of P and Mo. Key words: Beach pea, Lathyrus maritimus L., protein, amino acid, element, cold stress
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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 | 0.000 |
| 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) | 0.001 | 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".