Meteorological and Firn data from DYE-2, Greenland Ice Sheet, Summer 2016
Bibliographic record
Abstract
This dataset contains time-domain reflectometry (TDR), thermistor, and automatic weather station (AWS) data from summer, 2016 at DYE-2 on the southwestern Greenland Ice Sheet. Two firn pits were excavated, 5.3 and 2.2 m deep, and vertical arrays of TDR sensors and thermistors were installed to track meltwater infiltration and refreezing through the summer melt season (Samimi et al., 2020). AWS, TDR, and thermistor data were written to a Campbell Scientific CR1000 datalogger and the raw (ascii) output from the datalogger is imported to an Excel spreadsheet. Data have been quality-controlled through the elimination of any non-physical values (e.g., -99999); these are replaced with NaN or blanks in the dataset. The thermistor and AWS data are complete. Ultrasonic depth gauge (SR50A) data are filtered to remove non-physical values and erroneous data associated with spurious reflections from blowing snow.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".