Complementary Geochemical, mineralogical and microbiological analyses of materials collected on the Greenland Ice Sheet
Bibliographic record
Abstract
This dataset comprises of geochemical, mineralogical and microbiological analyses of material collected on the southwestern margin of the Greenland Ice Sheet in 2016 and 2017. Stream water, melted ice and snow samples were collected and analysed for carbon, nitrogen, phosphorus, cation and anion concentrations, pH, conductivity, total dissolved solids (TDS), mineral phase and class abundances and Rare Earth Elements (REE). Microbial community composition was also analysed. In addition, the results of a nutrient incubation experiment are also presented.The data were collected as part of a project investigating drivers of glacial ice algal growth on the Greenland Ice Sheet. We acknowledge funding from UK Natural Environment Research Council Consortium Grant, Black and Bloom (NE/M020770/1, NE/M021025/1 and NE/S001670/1). LGB and SL acknowledge funding from the German Helmholtz Recruiting Initiative (award number: I-044-16-01). LGB, AMA, and MT were also supported through an ERC Synergy Grant (ʻDeep Purpleʼ grant # 856416) from the European Research Council (ERC)
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.013 |
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".