Data_Sheet_1_Combined Carbon and Albedo Climate Forcing From Pine and Switchgrass Grown for Bioenergy.pdf
Bibliographic record
Abstract
<p>Expanding and restoring forests decreases atmospheric carbon dioxide, a natural solution for helping mitigate climate change. However, forests also have relatively low albedo compared to grass and croplands, which increases the amount of solar energy they absorb into the climate system. An alternative natural climate solution is to replace fossil fuels with bioenergy. Bioenergy crops such as switchgrass have higher albedo than forest ecosystems but absorb less total carbon over their lifetime. To evaluate trade-offs in the mitigation potential by pine and switchgrass ecosystems, we used eddy covariance net ecosystem exchange and albedo observations collected from planted pine forests and switchgrass fields in eastern North America and Canada to compare the net radiative forcing of these two ecosystems over the length of typical pine rotation (30 years). We found that pine had a net positive radiative forcing (warming) of 5.4 ± 2.8 Wm<sup>−2</sup> when albedo and carbon were combined together (30 year mean). However the assumptions regarding the fate of harvested carbon had an important effect on the net radiative forcing. When we assumed all switchgrass carbon was emitted to the atmosphere while the harvested pine carbon was prevented from entering the atmosphere, the 30-year mean net radiative forcing reversed direction (−3.6 ± 2.8 Wm<sup>−2</sup>). Overall, while the pine ecosystem absorbed more carbon than the switchgrass, the difference in albedo was large enough to result in similar climate mitigation potential at the 30-year horizon between the two systems, whereby the direction and magnitude of radiative forcing depends on the fate of harvested carbon.</p>
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 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 | 0.000 | 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 | 0.001 | 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) | 0.551 | 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".