Coastal tundra heath responses to experimental flooding and warming, Yukon-Kuskokwim Delta (western Alaska, United States), 2022-2024
Bibliographic record
Abstract
While the Arctic warms rapidly, several coastal tundra regions face increasing threats from altered flooding regimes. Yet, how flooding shapes coastal tundra ecosystems remains largely unknown. We experimentally examined how increased tidal flooding, under both ambient and elevated temperatures, influences key drivers of ecosystem functioning: micro-environment, vegetation, and organic matter decomposition. Data were collected across three summers (2022-2024) in a low-Arctic coastal tundra heath of the Yukon-Kuskokwim Delta (Alaska) – one of the largest high-latitude riverine deltas in North America. In May 2022, soon after snowmelt, we selected seven blocks within the focal tundra heath. Each block contained six plots, for a total of 42 plots. Plots within blocks were randomly assigned to a factorial combination of experimental monthly tidal floods (three levels: no-flooding, low-severity flooding, and high-severity flooding) and experimental warming (two levels: ambient and higher temperatures). We focused on three response categories: (1) micro-environmental changes, including air and soil temperatures, soil active layer thickness, redox potential, salinity, potential of hydrogen (pH), and chemistry; (2) vegetation responses, such as aboveground community composition and biomass, plant height, and root production; and (3) responses of organic matter decomposition (mass loss, decomposition rate, and stabilization factor).
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".