Impacts of inadequate historical disturbance data in the early twentieth century on modeling recent carbon dynamics (1951–2010) in conterminous U.S. forests
Bibliographic record
Abstract
Abstract Stand age and disturbance data have become more available in recent years and can facilitate modeling studies that integrate and quantify effects of disturbance and nondisturbance factors on carbon dynamics. Since high‐quality disturbance and forest age data to support forest dynamic modeling are lacking before 1950, we assumed dynamic equilibrium (carbon neutrality) for the starting conditions of forests with unknown historical disturbance and forest age information. The impacts of this assumption on forest carbon cycle estimation for recent decades have not been systematically examined. In this study, we tested an assumption of disequilibrium conditions for forests with unknown disturbance and age data by randomly assigning ages to them in the model initial year (1900) and analyzed uncertainties for 1951–2010 carbon dynamic simulations compared with the equilibrium assumption. Results show that with the dynamic equilibrium assumption, the total net biome productivity (NBP) of conterminous U.S. forests was 188 ± 60 Tg C yr−1 with 185 ± 56 Tg C yr−1 in living biomass and 3 ± 23 Tg C yr−1 in soil. The C release due to disturbance on average was about 68 ± 55 Tg C yr−1. The disequilibrium assumption causes annual NBP from 1951 to 2010 in conterminous U.S. forests to vary by an average of 13% with the largest impact on the soil carbon component. Uncertainties related to nondisturbance factors have relatively small impacts on NBP estimation (within 10%), while uncertainties related to disturbances cause biases in NBP of 4 to 28%. We conclude that the dynamic equilibrium assumption for the model initialization in 1900 is acceptable for simulating 1951–2010 forest carbon dynamics as long as disturbance and age data are reliable for this later period, although caution should be taken regarding the prior‐1950 simulation results because of their greater uncertainties.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".