The level of relatedness affects self/nonself discrimination in <i>Eucalyptus urophylla</i> seedlings
Bibliographic record
Abstract
Plants can develop differentially because of their ability of self/nonself discrimination and the degree of kinship among them. Here, we evaluate the ability of self/nonself discrimination of Eucalyptus urophylla S.T. Blake roots in plant groups with different levels of relatedness. We used three plant groups: clonal, half-siblings, and population. Split-root plants were grown in pots containing either two roots of the same plant (SD) or of two different plants (NSD). The growth of root and leaves of the half-siblings and population plants was decreased in NSD in relation to SD, whereas this response was not observed in the clonal group. The multivariate analysis indicated that there was a progressive increase in plant responses likely caused by competitive interaction of roots, as the level of relatedness between individuals was lower. Our results suggest that the group of clonal plants minimized the competitive interaction among them, indicating low ability to discriminate from each other. However, half-sibling and populational plants reduced growth as a result of root competition, showing high capacity of self discrimination. Thus, a minimum degree of genetic variation between plants seems necessary for kin recognition to be expressed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".