Modelling changes in nitrogen cycling to sustain increases in forest productivity under elevated atmospheric CO <sub>2</sub> and contrasting site conditions
Bibliographic record
Abstract
Abstract. If increases in net primary productivity (NPP) caused by rising concentrations of atmospheric CO2 (Ca) are to be sustained, key N processes such as soil mineralization, biological fixation, root uptake and nutrient conservation must also be increased. Simulating the response of these processes to elevated Ca is therefore vital for models used to project the effects of rising Ca on NPP. In this modelling study, hypotheses are proposed for changes in soil mineralization, biological fixation, root nutrient uptake and plant nutrient conservation with changes in Ca. Algorithms developed from these hypotheses were tested in the ecosystem model ecosys against changes in N and C cycling measured over several years under ambient vs. elevated Ca in Free Air CO2 Enrichment (FACE) experiments in the USA at the Duke Forest in North Carolina, the Oak Ridge National Laboratory forest in Tennessee, and the USDA research forest in Wisconsin. More rapid soil N mineralization was found to be vital for simulating sustained increases in NPP measured under elevated vs. ambient Ca at all three FACE sites. This simulation was accomplished by priming decomposition of N-rich humus from increases in microbial biomass generated by increased litterfall modelled under elevated Ca. Greater nonsymbiotic N2 fixation from increased litterfall, root N uptake from increased root growth, and plant N conservation from increased translocation under elevated Ca were found to make smaller contributions to simulated increases in NPP. However greater nutrient conservation enabled larger increases in NPP with Ca to be modelled with coniferous vs. deciduous plant functional types. The effects of these processes on productivity now need to be examined over longer periods under transient rises in Ca and a greater range of site conditions.
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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 | 0.001 |
| 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".