Effect of soil temperature on nitrogen fixation in Alnus
Bibliographic record
Abstract
Purpose: The short growing season and cold climate of the boreal forest can restrict soil nitrogen availability, limiting plant growth and ecosystem productivity. Vascular nitrogen-fixing plants should have an advantage in low nitrogen environments. Yet, their abundance in the boreal forest is low. How nitrogen fixation is affected when temperature differences occur between the soil and air, especially in the spring when soil temperatures remain cool, has not been documented in actinorhizal shrubs. Methods: A lab study was performed on Alnus alnobetula subsp. crispa (Aiton) Raus. For 13 weeks, soil was kept at either 10˚C, 14˚C or 16˚C, independently of shoot temperature, at 20˚C. Results: Soils at 14˚C and 10˚C inhibited whole-plant nitrogen fixation (by 53% and 68%) and photosynthesis (by 43% and 39%), respectively, compared to soils at 16˚C. Reductions in photosynthetic rate were mainly attributed to a reduction in the fixed nitrogen supply and subsequent reduction in chlorophyll formation. Photosynthesis was not reduced immediately, suggesting some utilization of a nitrogen source not supplied from current fixation. Reduced amounts of fixed nitrogen and photosynthates resulted in diminished biomass production and relative growth rate. Conclusion: The assumed advantages of being a nitrogen-fixing plant in a low nitrogen environment may be constrained by soil temperature to a larger extent than previously considered. This may restrict the abundance of nitrogen-fixing species in the boreal forest.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".