Pan-Arctic land-ice and tundra meltwater discharge database from 1950 to 2021
Bibliographic record
Abstract
A high resolution (daily, 250m) land ice and tundra meltwater discharge dataset for the period 1950-2021 in an easily accessible and storage efficient database covering most of the Arctic and sub-Arctic North Atlantic (SNA) ocean region, i.e. Canadian Arctic Archipelago, Greenland, Iceland, Svalbard, Russian Arctic Islands. We distribute 3 files for each Randolph Glacier Inventory v.6.0 first order region studied. These regions are RGI-03 Arctic Canada North (CanadaN), RGI-04 Arctic Canada South (CanadaS), RGI-05 Greenland (Greenland), RGI-06 Iceland (Iceland), RGI-07 Svalbard and Jan Mayen (Svalbard), RGI-09 Russian Arctic (RussiaN). In addition, we distribute a single pan-Arctic file (panArctic_MonthlyBasinRunoff.nc). Meltwater discharge is derived from daily ~6 km regional climate model, Modéle Atmosphérique Régional (MAR), runoff simulations that are statistically downscaled and routed to the coastlines. The statistical downscaling algorithm uses native vertical gradients of the MAR data and high resolution (250 m) DEM, land mask (Copernicus GLO-90) and ice mask (GIMP, RGI) datasets. Routing to coastal outflow points is performed by a hydrological routing scheme applied to the high-resolution DEM and the downscaled runoff. Meltwater components from non-glaciated land, bare glacier ice and glaciated area above the snowline are separated to facilitate further analysis.
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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 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".