A scientific synthesis and assessment of the Arctic Carbon Cycle
Bibliographic record
Abstract
The Arctic Monitoring and Assessment Programme (AMAP), along with the Climate and Cryosphere (CliC) Project and the International Arctic Science Committee (IASC), sponsored the Arctic Carbon Cycle Assessment Workshop, at the Red Lion Hotel in Seattle, Wash., between 27 February and 1 March 2007. The workshop was held in a general effort toward the scientific synthesis and assessment of the Arctic system carbon cycle, as well as to generate feedback on the working draft of an assessment document. The initial assessment was prepared by the Arctic carbon cycle assessment writing team, which is led by A. David McGuire (University of Alaska Fairbanks) and includes Leif Anderson (Goteborg University, Sweden), Torben Christensen (Lund University, Sweden), Scott Dallimore (Natural Resources Canada), Laodong Guo (University of Southern Mississippi), Martin Heimann (Max Planck Institute, Germany), Robie MacDonald (Department of Fisheries and Oceans, Canada), and Nigel Roulet (McGill University, Canada). The workshop brought together leading researchers in the fields of terrestrial, marine, and atmospheric science to report on and discuss the current state of knowledge on contemporary carbon stocks and fluxes in the Artie and their potential responses to a changing climate. The workshop was attended by 35 scientists representing institutions from 10 countries in addition to two representatives of the sponsor agencies (John Calder for AMAP and Diane Verseghy for CliC).
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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 | 0.043 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.028 | 0.022 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".