Kin recognition, not competitive interactions, predicts root allocation in young <i>Cakile edentula</i> seedling pairs
Bibliographic record
Abstract
• Recent studies have demonstrated sibling vs stranger differences in group root allocation in plants, suggesting that plants have the potential for kin discrimination in competition. However, morphology differences could potentially be generated by competition-based mechanisms. Here, we tested these hypotheses for the sibling vs stranger differences in root allocation in Cakile edentula. • Seeds were planted in pairs of either kin (siblings) or strangers, from all combinations of eight families, to give eight kin (sibling) and 28 stranger pair identities. Because the species has a seed dimorphism, the 10 replicates of each pair identity included both seed types. Root allocation, size inequality between seedlings in a pair, and competitive ability were derived from measures of biomass and height. • Cakile edentula seedlings demonstrated the same kin recognition response previously observed in juvenile plants, with lower root allocation in kin pairs than stranger pairs. The seed dimorphism was not associated with root allocation. • The two competitive mechanisms, genetic differences in competitive ability and increased size inequality in stranger groups, did not explain the root allocation differences in these seedlings. Kin recognition offered the most probable explanation for the differences in root allocation between sibling and stranger pairs.
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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 | 0.001 |
| 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) | 0.002 | 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".