Effect of nitrogen fertilizer, root branch order and temperature on respiration and tissue N concentration of fine roots in Larix gmelinii and Fraxinus mandshurica
Bibliographic record
Abstract
Root respiration is closely related to root morphology, yet it is unclear precisely how to distinguish respiration-related root physiological functions within the branching fine root system. Root respiration and tissue N concentration were examined for different N fertilization treatments, sampling dates, branch orders and temperatures of larch (Larix gmelinii L.) and ash (Fraxinus mandshurica L.) using the excised roots method. The results showed that N fertilization enhanced both root respiration and tissue N concentration for all five branch orders. The greatest increases in average root respiration for N fertilization treatment were 13.30% in larch and 18.25% in ash at 6°C. However, N fertilization did not change the seasonal dynamics of root respiration. Both root respiration and root tissue N concentration decreased with increase in root branch order. First-order (finest) roots exhibited the highest respiration rates and tissue N concentrations out of the five root branch orders examined. There was a highly significant linear relationship between fine root N concentration and root respiration rate. Root N concentration explained >60% of the variation in respiration rate at any given combination of root order and temperature. Root respiration showed a classical exponential relationship with temperature, with the Q(10) for root respiration in roots of different branching orders ranging from 1.62 to 2.20. The variation in root respiration by order illustrates that first-order roots are more metabolically active, suggesting that roots at different branch order positions have different physiological functions. The highly significant relationship between root respiration at different branch orders and root tissue N concentration suggests that root tissue N concentration may be used as a surrogate for root respiration, simplifying future research into the C dynamics of rooting systems.
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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) | 0.000 | 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".