Holocene lake-based Arctic glacier and ice cap records
Bibliographic record
Abstract
This data is supplement to "Arctic glaciers and ice caps through the Holocene: A circumpolar synthesis of lake–based reconstructions" and summarizes 66 lake–based records of Holocene glacier and ice cap variations from seven Arctic regions: Alaska (n=6), Baffin Island, Canada (n=5), glaciers and ice caps (GICs) peripheral to the Greenland Ice Sheet (n=22), Iceland (n=5), the Scandinavian peninsula (n=20), Svalbard (n=7), and the Russian high Arctic (n=2) (plus one non-lake record). For each included lake record, glacier and ice cap status is defined in 100–year intervals from 12–0 kiloannum (ka), where 0 = glacially influenced, and 1 = a smaller than present or absent glacier or ice cap. Arctic–wide, the percent of glaciers and ice caps smaller than present or absent is summarized in 100–year intervals from 12–0 ka. Blank cells indicate time periods with no data available, or when the status of the glacier or ice cap was unknown or ambiguous. We assign each record a unique site identifier. See "Site Information" for additional metadata on each record including: region, site # in regional compilations, original reference(s), lake name, glacier or ice cap name, latitude, and longitude.
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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.027 | 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".