Bibliographic record
Abstract
Although 'cheaters' potentially destabilize the legume-rhizobium mutualism, we lack a comprehensive review of host-symbiont fitness correlations. Studies measuring rhizobium relative or absolute fitness and host benefit are surveyed. Mutant studies are tallied for evidence of pleiotropy; studies of natural strains are analyzed with meta-analysis. Of 80 rhizobium mutations, 19 decrease both partners' fitness, four increase both, two increase host fitness but decrease symbiont fitness and none increase symbiont fitness at the host's expense. The pooled correlation between rhizobium nodulation competitiveness and plant aboveground biomass is 0.65 across five experiments that compete natural strains against a reference, whereas, across 14 experiments that compete rhizobia against soil populations or each other, the pooled correlation is 0.24. Pooled correlations between aboveground biomass and nodule number and nodule biomass are 0.76 and 0.83. Positive correlations between legume and rhizobium fitness imply that most ineffective rhizobia are 'defective' rather than 'defectors'; this extends to natural variants, with only one significant fitness conflict. Most studies involve non-coevolved associations, indicating that fitness alignment is the default state. Rhizobium mutations that increase both host and symbiont fitness suggest that some plants maladaptively restrict symbiosis with novel strains.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".