STABILIZING MECHANISMS IN A LEGUME-RHIZOBIUM MUTUALISM
Why this work is in the frame
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Bibliographic record
Abstract
Preferential rewarding of more beneficial partners may stabilize mutualisms against the invasion of less beneficial, that is cheater, genotypes. Recent evidence suggests that both partner choice and sanctioning may play roles in preventing the invasion of less-beneficial rhizobia in legume-rhizobium mutualisms. The importance of these mechanisms in natural communities, however, remains unclear. We grew 12 Medicago truncatula maternal families with a mixture of three rhizobium strains from their native range for three plant generations and estimated the symbiotic benefits (nodule number and size) conferred to each rhizobium strain. In this experiment, the majority of M. truncatula genotypes formed more nodules with more beneficial rhizobium strains, providing evidence for adaptive partner choice. We also found that three generations of symbiosis resulted in an increase in the relative frequency of rhizobium strains that were most beneficial to plants--suggesting that partner choice affects rhizobium fitness. By contrast, we found no evidence that plants differentially rewarded rhizobia postnodulation via sanctioning leading to differences in nodule size. Taken together, our data suggest that plants have evolved to recognize beneficial rhizobial signals during the early stages of symbiosis, and that signaling between plants and rhizobia may be subject to coevolutionary pressures.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it