Transect-based ice thickness, snow depth, and surface elevation data collected on the Yukon and Tanana rivers in Alaska during the late winter 2024
Bibliographic record
Abstract
As part of the Fresh Eyes on Ice project (https://fresheyesonice.org/), we traveled between the communities of Galena and Fairbanks Alaska during March of 2024 collected field data at 24 river locations and one lake location to document late winter ice and snow conditions. The majority of locations were selected based on satellite-observed ice conditions during freeze-up (October - December 2023) to represent transitions in ice formation timing and process. All data was collected along linear transects ranging from approximately 100 to greater than 1000-meter (m) length typically arrayed across freeze-up transitions zones using a combination of ice drilling at three to five points along each transect, high-frequency ice penetrating radar (500 millihertz (mHz) antennae), evenly spaced snow depth measurements using a magna probe, and unpersonned aerial vehicle (UAS) structure-for-motion photographs to construct relative surface elevation models. Ice thickness, snow depth, and relative surface elevation data were summarized to 10-m intervals along these transects. Specifically, these data are intended to aid understanding of how river freeze-up conditions are expressed in late winter at a resolution comparable to many field studies and remotely sensed data acquisition types. Several locations were selected with input from the communities of Galena, Tanana, and Rampart.
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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".