Nitrogen fertilisation influences low CO2 effects on plant performance
Bibliographic record
Abstract
Low atmospheric CO2 conditions prevailed for most of the recent evolutionary history of plants. Such concentrations reduce plant growth compared with modern levels, but low-CO2 effects on plant performance may also be affected by nitrogen availability, since low leaf nitrogen decreases photosynthesis, and CO2 concentrations influence nitrogen assimilation. To investigate the influence of N availability on plant performance at low CO2, we grew Elymus canadensis at ambient (~400 μmol mol-1) and subambient (~180 μmol mol-1) CO2 levels, under four N-treatments: nitrate only; ammonium only; a full and a half mix of nitrate and ammonium. Growth at low CO2 decreased biomass in the full and nitrate treatments, but not in ammonium and half plants. Low CO2 effects on photosynthetic and maximum electron transport rates were influenced by fertilisation, with photosynthesis being most strongly impacted by low CO2 in full plants. Low CO2 reduced stomatal index in half plants, suggesting that the use of this indicator in paleo-inferences can be influenced by N availability. Under low CO2 concentrations, nitrate plants discriminated more against 15N whereas half plants discriminated less against 15N compared with the full treatment, suggesting that N availability should be considered when using N isotopes as paleo-indicators.
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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 | 0.000 |
| 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 | 0.001 |
| 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".