Collaborative Research: Sensitivity of Circum-Arctic Peatland Carbon to Holocene Warm Climates and Climate Seasonality
Bibliographic record
Abstract
Collaborative Research: RUI: Sensitivity of Circum-Arctic Peatland Carbon to Holocene Warm Climates and Climate Seasonality (PIs: Zicheng Yu, David Beilman, and Philip Camill) Recent accelerated Arctic warming has caused widespread changes in terrestrial ecosystems, including carbon dynamics. Past climate warming and documented ecosystem responses provide crucial insights from Earth?s history for understanding and projecting possible responses to future climate change. In this project, researchers from Lehigh University, University of Hawaii and Bowdoin College will evaluate the outcomes of ?natural climate-warming experiments? that have occurred across the Arctic over the Holocene (the last 12,000 years). They will focus on two warm climate intervals: (1) the Holocene Thermal Maximum ranging in timings between 10,000 and 6000 years ago, and (2) the warm Medieval Climate Anomaly around 1000 years ago. The funds were requested (1) to collect new samples from carbon-rich peatlands from several critical regions (including Alaska, Mackenzie Basin, Hudson Bay Lowlands, Labrador, and Kamchatka), (2) to carry out integration and synthesis of available data, and (3) to work on climate-carbon modeling experiments along with their collaborators. The idea that both temperature and climate seasonality are dominant controls of carbon balances in carbon-rich Arctic ecosystems has important implications for projecting the fate of Arctic carbon in the future, as future warming is expected mainly in the winter season. Other broader impacts include (1) the support of new faculty career development, (2) training of undergraduate students, graduate students, and a postdoctoral fellow, emphasizing groups traditionally underrepresented in the natural sciences, (3) international collaborations and training through two workshops, and (4) public outreach through a symposium on Arctic climate change and soil carbon dynamics and the development of long-term exhibits at the Peary-MacMillan Arctic Museum at Bowdoin College.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".