Influence of long-term nutrient optimization on biomass, carbon, and nitrogen acquisition and allocation in Norway spruce
Bibliographic record
Abstract
We examined the effects of a long-term nutrient-optimization treatment on the acquisition and allocation of biomass, carbon (C), and nitrogen (N) in young Norway spruce (Picea abies (L.) Karst.) growing in northern Sweden. After 12 years of fertilization the absolute biomass of stem, needles, living branches, and stump and coarse roots was more than doubled by nutrient optimization (irrigation liquid fertilization treatment, IL), but the standing biomass of fine and small roots was unaffected compared with that of control trees. Biomass allocation among aboveground organs was not plastic to nutrient optimization and only the relative proportion of dead branches was reduced by nutrient optimization. Within the crown, biomass allocation to living branches was shifted towards the apex in IL trees. The N content in IL trees was substantially higher than in control trees. Most of the total N was allocated to needles and most of the needle N was found in the middle stratum of the living crown in both treatments, although the N concentration of current-year and older needles increased towards the apex in IL trees but not in control trees. The C concentration in the biomass components was not affected by the optimized fertilization. The results clearly show that there is a large potential to increase biomass production of Norway spruce (C sequestration) in the Nordic countries. This would secure the supply of raw material for the forest industry at the same time as the demand for biofuel from forest biomass is increasing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".