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 plant translocation must be hastened. 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 uptake and plant translocation 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. elevatedCa in Free Air CO2 Enrichment (FACE) experiments at the Duke Forest in North Carolina, the Oak Ridge National Laboratory forest in Tennessee, and the USDA research forest in Wisconsin, USA. Simulating more rapid soil N mineralization was found to be vital for modelling 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. Simulating more rapid nonsymbiotic N2 fixation, root N uptake and plant N translocation under elevated Ca was found to make much smaller contributions to modelled increases in NPP, although such contributions might be greater over longer periods and under more N-limited conditions than those simulated here. Greater increases in NPP with Ca were also modelled with increased temperature and water stress, and with coniferous vs. deciduous plant functional types. These increases were also associated with changes in N cycling.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".