An Arctic watershed observatory at Lake Peters, Alaska: weather–glacier–river–lake system data for 2015–2018
Bibliographic record
Abstract
Abstract. Datasets from a 4-year monitoring effort at Lake Peters, a glacier-fed lake in Arctic Alaska, are described and presented with accompanying methods, biases, and corrections. Three meteorological stations documented air temperature, relative humidity, and rainfall at different elevations in the Lake Peters watershed. Data from ablation stake stations on Chamberlin Glacier were used to quantify glacial melt, and measurements from two hydrological stations were used to reconstruct continuous discharge for the primary inflows to Lake Peters, Carnivore and Chamberlin creeks. The lake's thermal structure was monitored using a network of temperature sensors on moorings, the lake's water level was recorded using pressure sensors, and sedimentary inputs to the lake were documented by sediment traps. We demonstrate the utility of these datasets by examining a flood event in July 2015, though other uses include studying intra- and inter-annual trends in this weather–glacier–river–lake system, contextualizing interpretations of lake sediment cores, and providing background for modeling studies. All DOI-referenced datasets described in this paper are archived at the National Science Foundation Arctic Data Center at the following overview web page for the project: https://arcticdata.io/catalog/view/urn:uuid:df1eace5-4dd7-4517-a985-e4113c631044 (last access: 13 October 2019; Kaufman et al., 2019f).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.010 |
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".