Funding research using climate change mitigation: The case of the Carbone boréal research infrastructure
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 CO 2 -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 CO 2 -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 CO 2 removal from the atmosphere, NBS also render valuable ecosystem services such as biodiversity conservation, water and nutrient cycling regulation and soil preservation [3][4][5][6].Several positive impacts on human well-being and sustainable development goals can also be achieved through NBS [1].One of the widely applied NBS is afforestation.Hence, numerous commitments have been pledged to plant billions of trees [7,8] but general concerns remain on the net efficiency relative to costs.Such afforestation efforts demand long-term research that must be financed accordingly but that does not match with conventional funding usual timeframes.One of the enticing prospects is in using offset markets to fund ecosystem conservation and rehabilitation projects, as recognized in the 2001 Clean Development Mechanism of the United Nations Framework Convention on Climate Change.However, claims of additional capture and permanence are subject to many inaccuracies and risks.While it is relatively simple to estimate the amount of carbon stored in trees, the dynamics of its accumulation and its conservation in ecosystems over time is more uncertain and must be assessed by combining modelling methods and field measurements.This is especially important and time sensitive in slow-growing forests such as the boreal forest where decades are necessary to confirm or invalidate the projections.This poses two main challenges: first, establishing study plots that can be monitored to answer specific questions over several decades; second, ensuring that research is funded for the duration, which is unusual at best for conventional research grant programs.
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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 | 0.028 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".