McMurdo Dry Valleys LTER - Landscape Albedo - Taylor Valley, Antarctica - 2015 to 2019
Bibliographic record
Abstract
This data package contains reflectance data and associated aerial images collected using a helicopter-suspended "albedo box," in which a shortwave radiometer and camera where mounted facing downward. The purpose of this study was to measure how surface reflectance varies within and across landscape types (glaciers, lakes, and soils) over the course of a single field season as well as across multiple field seasons. We made five flights in the 2015-2016 field season (20 November 2015, 7 December 2015, 24 December 2015, 5 January 2016, and 12 January 2016), five flights in the 2016-2017 field season (11 November 2016, 3 December 2016, 14 December 2016, 3 January 2017, 23 January 2017), four flights in the 2017-2018 field season (22 November 2017, 7 December 2017, 27 December 2017, 13 January 2018), and two flights in the 2018-2019 field season (23 November 2018, 9 January 2019). Flights originated from Lake Hoare field camp, flew down-valley over Canada Glacier, Lake Fryxell, and Commonwealth Glacier, then turned around and flew up-valley to Taylor Glacier Meteorological Station, after which they returned to Lake Hoare field camp. Flights took roughly one hour and were flown at approximately 25.72 m s-1 (50 knots) and 91.44 m (300 ft) above the ground surface. These data can be normalized to incoming solar radiation (measured in-situ at meteorological stations) to calculate landscape albedo. When collected several times throughout a season, these results can show how snow distribution and physical changes to glacier and lake ice impact the amount of incoming radiation that is absorbed, while also tracking the influence of deposited sediment on ice surfaces. Moreover, these data are important for quantifying the long-term changes in energy connectivity between the atmosphere and the landscape (i.e., addressing H1 of MCM V).
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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.020 | 0.009 |
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".