Grain Size Distribution in the Subsurface Sediments of Floodplains Across the Yukon River Basin, Alaska, 2022
Bibliographic record
Abstract
To characterize the sedimentological properties of riverbank materials in the Yukon River Basin, we analyzed grain size distributions from bank sediments collected at two representative sites: the Koyukuk River near Huslia and the Yukon River near Beaver. These locations, situated within discontinuous permafrost regions, were selected to capture spatial variability in depositional environments and inform interpretations of fluvial processes, sediment transport dynamics, and sediment-associated carbon cycling in high-latitude floodplains. In addition to their geomorphic relevance, these sediments serve as reservoirs for organic carbon and potentially toxic elements, such as mercury. Associated organic carbon data are reported in Ke et al. 2024, doi.org/10.18739/A22R3NZ79, and mercury concentrations are archived in Isabel et al. 2023, doi.org/10.18739/A2WW7720N. As part of a National Science Foundation (NSF)-funded effort to assess erosion and biogeochemical cycling in the Yukon River Basin, sediment samples were collected between June and September 2022 from the Yukon River near Beaver (65.700 degrees North [°N], 156.387 degrees West [°W]) and the Koyukuk River near Huslia (66.362 °N, 147.398 °W). Grain size distributions were analyzed using laser diffraction analysis with a Mastersizer 3000E, and are reported here as statistical summaries.
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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) | 0.001 | 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".