Forests, Carbon, and the Global Environment: New Directions in Research
Bibliographic record
Abstract
At this time, there is a convergence between two related and serious global concerns: (1) the emerging climate crisis brought on by fossil fuel combustion and land-use change and (2) an economy reliant on increasingly scarce and nonrenewable fossil fuels for energy and materials. Both of these concerns are pushing science and policy to begin discussing and understanding the implications of a future economy in a carbon (C)-constrained world, where both opportunities and challenges abound (World Economic Forum 2009). The two concerns are related. First, there is clear evidence that climate change is caused by the human use of fossil C (for energy and feedstock for materials, such as plastic) and deforestation (IPCC 2007b). In turn, climate change has potentially profound effects on C storage in agriculture and forests. The need to mitigate climate change has created political and policy pressure to reduce the use of fossil C through the development of renewable fuels and materials from biological feedstocks, mostly from land-based biomass in crops and forests (IPCC 2007a). In addition, land dedicated to agriculture that is threatened by climate change will be increasingly threatened by competition to grow biomass feedstocks (Rathmann, Szklo, and Schaeffer 2010). Indeed, some common crops used traditionally as a food source are being reengineered for fuels: corn, soybeans, oil palm, and sugarcane, to name a few (Naylor et al. 2007). Moreover, land once devoted to agriculture is increasingly being converted to nonagricultural biomass for fuel and materials. Natural forests are being converted to biofuel feedstock plantations (Danielsen et al. 2008). Therefore, the two concerns are intimately related to land-use change and its relationship to the C cycle.
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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".