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Mass balance of the Greenland Ice Sheet from 1992-2018

2020· dataset· en· W6913001856 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEdinburgh Research Explorer (University of Edinburgh) · 2020
Typedataset
Languageen
Field
Topic
Canadian institutionsUniversity of TorontoMemorial University of Newfoundland
Fundersnot available
KeywordsGreenland ice sheetIce sheetCryosphereGlacier mass balanceFuture sea levelIce capsIce-sheet modelGroenlandiaClimate change

Abstract

fetched live from OpenAlex

This dataset consists of the time series of mass change of the Greenland Ice Sheet and its contribution to global sea level between 1980 and 2018 derived from satellite measurements. The dataset presented here is a reconciled estimate of mass balance estimates from three independent satellite-based techniques - gravimetry, altimetry and input-output method - and its associated uncertainty. This dataset is part of the Ice Sheet Mass Balance Inter-comparison Exercise (IMBIE). The total mass change as well as the partition between surface and dynamics mass balance are provided in this dataset. This work is an outcome of the Ice Sheet Mass Balance Inter-Comparison Exercise (IMBIE) supported by the ESA Climate Change Initiative and the NASA Cryosphere Program. Andrew Shepherd was additionally supported by a Royal Society Wolfson Research Merit Award and the UK Natural Environment Research Council Centre for Polar Observation and Modelling (cpom30001).

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Open science, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.028
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.005
Science and technology studies0.0010.004
Scholarly communication0.0000.001
Open science0.0090.005
Research integrity0.0010.005
Insufficient payload (model declined to judge)0.0290.001

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.

Opus teacher head0.092
GPT teacher head0.300
Teacher spread0.208 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it