Wetland plant functional trait responses to experimental warming and flooding, Yukon-Kuskokwim Delta (Western Alaska, USA) (2022-2023)
Bibliographic record
Abstract
This dataset was created to understand plant trait responses to warming and flooding in the Yukon-Kuskokwim (Y-K) Delta (western Alaska, USA). We conducted a two-year field experiment in which we passively increased temperatures, simulated periodic tidal flooding at two intensity levels (low and high) during the 2022 and 2023 summer growing season. Our treatments reflect changes expected in the Y-K Delta in the next 10-20 years. We conducted the experiment in a wet sedge-shrub meadow and only sampled the dominant species in this community. At the end of the 2023 season, we measured economics traits (specific leaf area, leaf dry matter content, specific stem density) and size-related traits (height, leaf area, leaf thickness) in four focal species: the dominant sedge Carex rariflora, the dominant deciduous dwarf-shrub Salix fuscescens, the most abundant grass, Calamagrostis canadensis, and the most abundant forb, Potentilla palustris. We also measured additional traits related to seasonal growth, reproduction, and reproductive phenology to capture species temporal responses to warming and flooding in the two most dominant species. These included vegetative height over time, number of reproductive structures, reproductive structure length, reproductive shoot height (Carex only), and reproductive phenological stages (Salix only).
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".