MétaCan
Menu
Back to cohort

Modelled and observational freshwater flux time series for land ice in the Arctic and North Atlantic for 1958-2016 b.

2018· dataset· en· W6969216653 on OpenAlexaboutno aff

Bibliographic record

VenueNERC Environmental Data Service · 2018
Typedataset
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsArcticSea iceArctic dipole anomalySurface runoffTundraNetCDFCryosphereArctic ice pack

Abstract

fetched live from OpenAlex

This dataset comprises modelled and observational solid and liquid coastal freshwater fluxes from land ice in Greenland, Canadian Arctic Archipelago, Svalbard and Iceland. Tundra runoff from all these land areas is also included. The fluxes have been routed to coastal grid cells around the margins of the land areas. The fluxes are provided in three fields: tundra, surface runoff over ice, solid ice discharge (icebergs, for Greenland only). The data are on a 5 km polar stereographic projection with a monthly time step and are in a netcdf format. Detailed description of the derivation of the data can be found in an associated paper in JGR-Oceans: Bamber J.L, et al "Land ice freshwater budget of the Arctic and North Atlantic Oceans. Part I: Data, methods and results". This dataset contains monthly resolution runoff and discharge from 01/01/1958 to 31/12/2016 and is an updated version of the data held at BODC (Bamber et al. 2018, doi:10.5285/643aa9bc-bcd6-45ad-e053-6c86abc07da0).

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.102
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.045
GPT teacher head0.246
Teacher spread0.200 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreDataset

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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueNERC Environmental Data ServiceFrench-language works237,207