Funding research using climate change mitigation: The case of the Carbone boréal research infrastructure
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Since 1988, the Intergovernmental Panel on Climate Change (IPCC) has gathered research and produced reports to inform decision makers on climate change. Among crosscutting topics, ecosystem management and nature-based solutions (NBS) have received growing attention as they are readily available and relatively inexpensive. NBS are part of the Agriculture, Forestry and Other Land Uses economical sector [1]. While carbon prices can reach up to USD100 t-1 CO2-eq, greenhouse gas (GHG) land-based mitigation can be achieved for less in agriculture (e.g. soil carbon management, agroforestry, soil biochar addition), forestry (e.g. afforestation, reforestation, decreased deforestation) and using other ecosystems (e.g. peatland and wetland restoration) [1]. NBS have the potential to reduce GHG emissions by 8–14 Gt CO2-eq yr-1 between 2020–2050 [1], which represents 32–82% of the emission gap by 2030 to limit global warming between 1.5–2°C by 2100 compared with the preindustrial era [2]. In addition to CO2 removal from the atmosphere, NBS also render valuable ecosystem services such as biodiversity conservation, water and nutrient cycling regulation and soil preservation [3–6]. Several positive impacts on human well-being and sustainable development goals can also be achieved through NBS [1].
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.
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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it