N/P/K Ratios and CO<sub>2</sub> Concentration Change Nitrogen-Photosynthesis Relationships in Black Spruce
Bibliographic record
Abstract
The relationship between photosynthesis and leaf nitrogen concentration is often used to model forest carbon fixation and ratios of different nutrient elements can modify this relationship. However, the effects of nutrient ratios on this important relationship are generally not well understood. To investigate whether N/P/K ratios and CO2 concentration ([CO2]) influence relationships between photosynthesis and nitrogen, we exposed one-year-old black spruce seedlings to two [CO2] (370 and 720 μmol·mol-1), two N/P/K ratio regimes (constant (CNR) and variable (VNR) nutrient ratio) at 6 N supply levels (10 to 360 μmol·mol-1). It was found that photosynthesis (Pn) was more sensitive to nitrogen supply and N/P/K ratios under the elevated [CO2] than under ambient [CO2]; under the elevated [CO2], Pn declined with increases in N supplies above 150 μmol·mol-1 in the CNR treatment but was relatively insensitive to N supplies of the same range in the VNR treatment. Further, our data suggest that the nutrient ratio and the CO2 elevation effects on photosynthesis were via their effects on the maximum rate of carboxylation (Vcmax) but not electron transport (Jmax) or triose phosphate utilization (TPU). The results suggest that the CO2 elevation increased the demand for all three nutrient elements but the increase was greater for N than for P and K. The CO2 elevation resulted in greater photosynthetic use efficiencies of N, P and K, but the increases varied with the nutrient ratio treatments. The results suggest that under elevated [CO2], higher net photosynthetic rates demand different optimal N-P-K ratios than under the current [CO2].
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".