New Hydrometeorological Instrument Cluster at Inglefield Land, NW Greenland
Bibliographic record
Abstract
Mass loss from the Greenland Ice Sheet (GrIS) is a primary contributor to sea level rise, but substantial uncertainty exists in estimates of future ice sheet losses. Surface mass balance (SMB) models, the current leading approach to sea level rise projection, anticipate continued dominance of runoff as a mass loss pathway. Despite their preeminence, SMB models in vulnerable northern environments lack adequate field validation, particularly for error-sensitive runoff estimates. We have installed a cluster of high quality field instruments at the Minturn Elv, a proglacial river site in Inglefield Land, NW Greenland to provide discharge and weather datasets for the validation and refinement of climate/SMB runoff models. The instrument cluster has meteorological, hydrological, and time lapse camera instrumentation, including a vented water level stage recorder, single shot and scanning lidars, time lapse cameras, and in situ ADCP discharge and terrestrial scanning lidar measurements. The instrument suite provides novel flow and weather datasets with the opportunity to evaluate experimental approaches to stage measurement in adverse, high-latitude areas. Inglefield is a uniquely advantaged location because proglacial runoff is dominated by SMB processes operating on the ice surface without interference from subglacial hydrology. Overall, our hydrometeorological instrument cluster at Inglefield Land will provide one of the few validation datasets for regional climate models outside of Southwest Greenland.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".