Density and ice layer stratigraphy in 24 shallow firn cores from Southwest Greenland, 2017 - 2019
Bibliographic record
Abstract
Observations of near-surface firn density and ice layer stratigraphy collected on the Greenland ice sheet are rare. This dataset contains information about twenty-four shallow firn cores drilled in the percolation zone in southwest Greenland between 2017 and 2019. Seven cores ranged between 2.9 and 6.3 meter (m) depth, with the remaining seventeen cores extending to 10–27 m depths. Each core has data on density, ice layer stratigraphy, and type of material (i.e. snow, firn, or ice). Quality control was performed on all data to replace erroneous data. More details including uncertainty analysis are provided in the method section and in Rennermalm et. al., (2021). The dataset includes the following files Core_meta_data.csv : File with metadata about each core, including location, site name, retrieval date, investigators, and other information Explanation_of_core_variables.csv : File with explanation of the variables reported in the core data files Core data files: For each core two files are included: a) a file with all field data with the following file naming convention [Site]-[Year]-[Core number], and b) a file only including the depth/thickness of all ice layers [Site]-[Year]-[Core number]_icelayer_data. Text in brackets are variables. References: Rennermalm, Å. K., Hock, R., Covi, F., Xiao, J., Corti, G., Kingslake, J., Leidman S. L., Miege, C., MacFerrin, M., Machguth, H., Osterberg, E., Kameda, T., McConnell. J. 2021. Shallow firn cores 1989 - 2019 in southwest Greenland’s percolation zone reveal decreasing density and ice layer volume after 2012. Journal of Glaciology, 1-12, https://doi.org/10.1017/jog.2021.102
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".