Protein content and protease activity in senescing roots and leaves of wetland monocot species with contrasting root turnover strategies
Bibliographic record
Abstract
Perennial herbaceous monocots in Northern Ontario wetlands can be classified in two distinct types of root turnover strategies: those with overwintering roots, and those with complete root mortality at the end of the growing season. All species have autumnsenescing leaves. The present thesis is part of investigations to understand adaptive advantages of the two strategy types, focusing on nutrient remobilization from senescing roots. Existing data on nutrient remobilization from senescing roots is based on changes in element content in dying roots, and do not differentiate between remobilization and leaching out. Root protein content and aminopeptidase activity was assessed for gardengrown plants of six species from September to November, three species with autumnsenescing roots (Rhynchospora alba, Sagittaria latifolia, Sparganium americanum) and three with overwintering roots (Carex oligosperma, Iris versicolor, Scirpus microcarpus). We hypothesized that protein degradation and protease activity would be higher in autumn-senescing roots. The results confirm the existence of two root turnover strategies, species with annual roots showing a decline in root protein content, while species with perennial roots did not show such a decrease. Leaf protein content deceased in all species but C. oligosperma, known to senesce late. Total root aminopeptidase activity per fresh mass decreased in species with annual roots, but not in those with perennial roots. In contrast to expectation, specific aminopeptidase activity did not change over time and did not differ between the strategies. We conclude that nitrogen remobilization is an active process in senescing roots, and in autumn occurs only in annual roots. However, temporal characterization of root enzyme activities requires more detailed investigations
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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 | 0.001 | 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".