Strategic expansion of the Arctic-Boreal carbon flux network
Bibliographic record
Abstract
Permafrost soils store vast amounts of carbon, twice as much as the atmosphere. With climate warming occurring at a rate three to four times the global average in Arctic-boreal ecosystems this carbon is at risk of being released to the atmosphere in the form of carbon dioxide or methane (hereby, carbon fluxes) exacerbating global climate warming. However, gaps in carbon flux data in high latitude ecosystems limit our ability to understand, upscale, model, and project carbon fluxes, which in turn limit our ability to set accurate emissions reduction targets to stay within globally agreed upon temperature thresholds such as 1.5 or 2°C. To address this, we are strategically expanding the informal Arctic-boreal carbon flux network through the installation of ~10 new eddy covariance sites and supporting expanded measurements (during winter and for CH4) at existing sites. To guide site selection decision making, we are using a representativeness analysis of the current eddy covariance network, determining the Euclidean distance in environmental data space using key carbon flux drivers at a 1 km2 resolution across the Arctic-boreal region (Pallandt et al., 2022). Analyses show a lack of representation in the high Arctic, Siberia, and Eastern Canada, and representation is substantially lower when considering only sites with year-round measurement or that measure methane, limiting our ability to estimate the full impact of carbon fluxes from the Arctic-boreal region. Additional consideration is given to logistical constraints, partnerships, and modeling gaps. Work has begun including a re-installation in Churchill, MB, and upgrades for year-round and additional instrumentation for 4 towers in Alberta and the Northwest Territories and a site in Iqaluit, NU. We will further synthesize existing network data to inform the Dynamic Vegetation [Model] Dynamic Organic Soil Terrestrial Ecosystem Model (DVM-DOS-TEM) model and use machine learning approaches to upscale Arctic-boreal carbon fluxes.
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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 | 0.013 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".