Pressure transducer data for beaver ponds and associated streams in northwestern Alaska 2021-2026
Bibliographic record
Abstract
This dataset contains water level, water temperature, and barometric pressure at Alaskan beaver ponds collected as part of the Arctic Beaver Observation Network. The Arctic Beaver Observation Network is a 5-year project (2021-2026) funded by the National Science Foundation. The natural science part of the project uses remote sensing to observe the progress and impacts of beaver engineering in the Arctic, starting in Alaska and extending into Canada and Eurasia. The project also establishes field sites at tundra beaver ponds to study the implications of beaver engineering on hydrology and permafrost, as well as pond evolution documented using Unmanned Aerial Systems (UAS). Remote sensing work will map beaver ponds over time. Field measurements at tundra beaver ponds are made in August and late March. Data generated by field measurements include water level and temperature from pressure-transducers, subsurface imaging from ground-penetrating radar, sonar measurements for beaver pond bathymetry, tabular data associated with water quality measurements, and ice thickness and water depth (in winter). Data is also posted from UAS surveys: annual visible and multi-spectral surveys, as well as snow depth.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| 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) | 0.019 | 0.023 |
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".