Charcoal, geochemical, and isotope records from Alaskan tundra lakes
Bibliographic record
Abstract
The archive includes lake-sediment chronologies, macroscopic charcoal, geochemical data (XRF, XRD, LOI, MS, isotopes), and/or water chemistry measurements from twelve lakes in tundra ecosystems in Alaska, associated with NSF grant ARC-1023477 (Feng Sheng Hu, PI). All datasets are in csv format (converted from original excel xlsm workbooks). Each lake has metatdata in the first tab that contains information on associated publications, lake location, water depth at the coring location, ecoregion, sediment-core length, and age span of sediments. These data are from lakes located in the following Alaskan tundra ecoregions: Noatak River Watershed, Yukon-Kuskokwin Delta, northern and southern areas of the Brooks Range, and the Arctic Foothills. Detailed methodological information can be found in the associated publications.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.064 | 0.011 |
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; both teacher heads agree on what is shown here.
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".