Bibliographic record
Abstract
Abstract. A growing body of evidence suggests that to achieve the temperature goals of the Paris Agreement, carbon dioxide removal (CDR) will likely be required in addition to massive carbon dioxide (CO2) emissions reductions. Nature-based CDR, which includes a range of strategies to sequester carbon in natural reservoirs, could play an important role in efforts to limit climate warming to well below 2 °C above preindustrial levels. Agricultural CDR could enhance soil carbon sequestration, though the climate efficacy of such methods remains uncertain. Here, we use an intermediate complexity climate model to perform simulations of agricultural CDR in the form of soil carbon sequestration at a range of possible rates for different costs under three future emissions scenarios. We found that plausible levels of agricultural CDR were able to reduce CO2 concentration by 5–19 ppm and global surface air temperature by 0.02–0.10 °C by the end of century. This temperature decrease was non-linear with respect to cumulative removals, as any carbon removed remained part of the active carbon cycle, lessening the climate benefit compared to if the removed carbon was permanently stored in geological reservoirs. CDR was found to be more effective at reducing surface air temperature in low emissions scenarios, but less effective at reducing atmospheric CO2, compared to high emissions scenarios. This was because the weaker CO2 sinks in a high CO2 world had a more muted response to removal, so a substantially higher proportion of carbon was removed from the atmosphere for a given amount of CDR in a higher emissions scenario. The enhanced temperature response to CDR in lower emissions scenarios was due to the logarithmic response of radiative effects to changes in CO2, where at low atmospheric CO2 concentrations, small changes in CO2 are more effective at changing the global radiative balance than at higher CO2 concentrations. CDR was substantially more effective when implemented at a higher rate, as CDR makes a proportionally larger difference in a climate with lower cumulative air fraction of CO2. Land and soil carbon responses were driven by the scenario-dependent balances between the impacts of CDR on primary productivity from CO2 fertilization, and the impacts on soil respiration from increased soil carbon availability and global temperatures.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.021 | 0.011 |
| Insufficient payload (model declined to judge) | 0.215 | 0.117 |
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".