Nitrogen fixation makes biomass allocation to roots independent of soil nitrogen supply
Bibliographic record
Abstract
Biomass allocation patterns in plants are known to be affected by soil nitrogen availability. Since nitrogen availability can depress symbiotic nitrogen fixation, and nitrogen fixation can make plant growth independent of soil nitrogen availability but is energetically costly, it is unclear how allocation patterns in nitrogen-fixing species should respond to variation in soil nitrogen availability. We examined the effect of nitrogen source and concentration on the growth and allocation patterns in the nitrogen-fixing shrub Alnus viridis subsp. crispa (Aiton) Turrill. Plants were grown with either NH4+-N or NO3–-N at a range of low N concentrations, from 0 to 2 mmol·L–1, and either inoculated with Frankia or not. Plants without nodules had 25.l% lower biomass and had double the allocation to roots at all but the 2 mmol·L–1nitrogen concentration. Even though nodulated plants increased growth with nitrogen concentration, allocation to roots as a fraction of total biomass did not vary in these plants, suggesting increased growth resulted from more efficient nitrogen acquisition. Allocation to roots was a significant predictor of plant growth in non-nodulated plants (r2= 0.318, for linear least squares fit with log mass) but not for nodulated plants (r2= 0.108). As nitrogen concentrations increased, allocation to nodules, specific nodule numbers, and the proportion of nitrogen fixed by the plants decreased, demonstrating a shift to soil nitrogen use.
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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 | 0.000 |
| 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".