Nitrogen addition does not influence pre‐infection partner choice in the legume–rhizobium symbiosis
Bibliographic record
Abstract
PREMISE OF THE STUDY: Resource mutualisms such as the symbiosis between legumes and nitrogen-fixing rhizobia are context dependent and are sensitive to various aspects of the environment, including nitrogen (N) addition. Mutualist hosts such as legumes are also thought to use mechanisms such as partner choice to discriminate among potential symbionts that vary in partner quality (fitness benefits conferred to hosts) and thus impose selection on rhizobium populations. Together, context dependency and partner choice might help explain why the legume-rhizobium mutualism responds evolutionarily to N addition, since plant-mediated selection that shifts in response to N might be expected to favor different rhizobium strains in different N environments. METHODS: We test for the influence of context dependency on partner choice in the model legume, Medicago truncatula, using a factorial experiments with three plant families across three N levels with a mixed inoculation of three rhizobia strains. KEY RESULTS: Neither the relative frequencies of rhizobium strains occupying host nodules, nor the size of those nodules, differed in response to N level. CONCLUSIONS: Despite the lack of context dependence, plant genotypes respond very differently to mixed populations of rhizobia, suggesting that these traits are genetically variable and thus could evolve in response to longer-term increases in N.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".