Potential effects of a high CO<sub>2</sub> future on leguminous species
Bibliographic record
Abstract
Why this research Matters Legumes provide an important source of food and feed due to their high protein levels and many health benefits, and also impart environmental and agronomic advantages as a consequence of their ability to fix nitrogen through their symbiotic relationship with rhizobia. As a result of our growing population, the demand for products derived from legumes will likely expand considerably in coming years. Since there is little scope for increasing production area, improving the productivity of such crops in the face of climate change will be essential. While a growing number of studies have assessed the effects of climate change on legume yield, there is a paucity of information regarding the direct impact of elevated CO2 concentration (e[CO2]) itself, which is a main driver of climate change and has a substantial physiological effect on plants. In this review, we discuss current knowledge regarding the influence of e[CO2] on the photosynthetic process, as well as biomass production, seed yield, quality, and stress tolerance in legumes, and examine how these responses differ from those observed in non‐nodulating plants. Although these relationships are proving to be extremely complex, mounting evidence suggests that under limiting conditions, overall declines in many of these parameters could ensue. While further research will be required to unravel precise mechanisms underlying e[CO2] responses of legumes, it is clear that integrating such knowledge into legume breeding programs will be indispensable for achieving yield gains by harnessing the potential positive effects, and minimizing the detrimental impacts, of CO2 in the future.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".