A digital archive of human activity in the McMurdo Dry Valleys, Antarctica
Bibliographic record
Abstract
Abstract. Over the last half century, the McMurdo Dry Valleys (MDV) of East Antarctica have become a globally important site for scientific research and environmental monitoring. Historical data can make important contributions to current research activities and environmental management in Antarctica, but tend to be widely scattered and difficult to access. We address this need in the MDV by compiling over 5,000 historical photographs, sketches, maps, oral interviews, publications, and other archival resources into an online digital archive. The data have been digitized and georeferenced using a standardized metadata structure, which enables intuitive searches and data discovery via an online interface. The ultimate aim of the archive is to create as comprehensive as possible a record of human activity in the MDV to support ongoing research, management, and conservation efforts. This is a valuable tool for scientists seeking to understand the dynamics of change in lakes, glaciers, and other physical systems, as well as humanistic inquiry into the history of the Southern Continent. In addition to providing benchmarks for understanding change over time, the data can help target field sampling for studies working under the assumption of a pristine landscape by enabling researchers to identify the date and extent of past human activities. The full database is accessible via a web browser-based interface hosted by the McMurdo Long Term Ecological Research site: http://mcmurdohistory.lternet.edu/ and the raw data are available at the Environmental Data Initiative https://doi.org/10.6073/pasta/6744cb28a544fda827805db123d36557 (Howkins et al., 2019).
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.025 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.046 | 0.012 |
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".