Alaska Community Ice Observations - 2019-2022
Bibliographic record
Abstract
This dataset contains community based observations of ice thickness throughout the winters of 2019/2020, 2020/2021, and 2021/2022. Observations are co-located within or near communities participating in the Fresh Eyes on Ice project, seeking to expand spatial coverage of ice observations and science literacy in classrooms around the state. This spatially distributed dataset provides valuable information about ice thickness through the winter for the period of 2019-2022 on lakes and rivers around Alaska, including Big Lake near Venetie, Alaska, Brown's Slough in Bethel, Alaska, Noyes Slough in Fairbanks, Alaska, the Tanana River near Fairbanks, Alaska, Shageluk Lake near Shageluk, Alaska, Third Lake near Noatak, Alaska, Alexander Lake in Galena, Alaska, Smith Lake near Fairbanks, Alaska, Big Trail Lake near Fairbanks, Alaska, Toolik Lake at Toolik Research Station, Alaska, Jan Lake near Tok, Alaska, Float Pond Lake near Nenana, Alaska, unnamed lakes in the communities of Kenny Lake, Alaska, Arctic Village, Alaska, Sleetmute, Alaska, and McGrath, Alaska, Pippen Lake near Tonsina, Alaska, Yukon River near Eagle, Alaska, Long Lake near Eagle Village, Alaska, Sculpin Lake near Chitina, Alaska, and Dog Pits Lakes near Fairbanks, Alaska . These observations were made by drilling of 3 holes in an undisturbed location at the site by students, teachers, and community members, as well as members of the Fresh Eyes on Ice project. Snow depth was recorded at each of the holes prior to drilling.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.014 |
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".