Inland waters and their role in the carbon cycle of Alaska
Bibliographic record
Abstract
Abstract The magnitude of Alaska ( AK ) inland waters carbon (C) fluxes is likely to change in the future due to amplified climate warming impacts on the hydrology and biogeochemical processes in high latitude regions. Although current estimates of major aquatic C fluxes represent an essential baseline against which future change can be compared, a comprehensive assessment for AK has not yet been completed. To address this gap, we combined available data sets and applied consistent methodologies to estimate river lateral C export to the coast, river and lake carbon dioxide ( CO 2 ) and methane ( CH 4 ) emissions, and C burial in lakes for the six major hydrologic regions in the state. Estimated total aquatic C flux for AK was 41 Tg C/yr. Major components of this total flux, in Tg C/yr, were 18 for river lateral export, 17 for river CO 2 emissions, and 8 for lake CO 2 emissions. Lake C burial offset these fluxes by 2 Tg C/yr. River and lake CH 4 emissions were 0.03 and 0.10 Tg C/yr, respectively. The Southeast and South central regions had the highest temperature, precipitation, terrestrial net primary productivity ( NPP ), and C yields (fluxes normalized to land area) were 77 and 42 g C·m −2 ·yr −1 , respectively. Lake CO 2 emissions represented over half of the total aquatic flux from the Southwest (37 g C·m −2 ·yr −1 ). The North Slope, Northwest, and Yukon regions had lesser yields (11, 15, and 17 g C·m 2 ·yr −1 ), but these estimates may be the most vulnerable to future climate change, because of the heightened sensitivity of arctic and boreal ecosystems to intensified warming. Total aquatic C yield for AK was 27 g C·m −2 ·yr −1 , which represented 16% of the estimated terrestrial NPP . Freshwater ecosystems represent a significant conduit for C loss, and a more comprehensive view of land‐water‐atmosphere interactions is necessary to predict future climate change impacts on the Alaskan ecosystem C balance.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".