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Record W7078739124 · doi:10.5281/zenodo.15388336

Pan-Arctic weather data set from publicly available in situ measurements from 1990-2023

2025· dataset· en· W7078739124 on OpenAlexaboutno aff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2025
Typedataset
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsnot available
FundersKøbenhavns Universitet
KeywordsShortwaveLongwaveData setSnowMetadataData qualityShortwave radiationCalibration

Abstract

fetched live from OpenAlex

These datasets are collected from publicly available in situ measurements of 36 ground and surface climatic variables from 16 different data sources, focusing on the period 1990-2023, but with some sources providing data much further back in time (early 1900s). The variables collected are: Air temperature, Snow depth, Precipitation, Longwave Incoming radiation, Longwave Outgoing Radiation, Shortwave Incoming Radiation, Shortwave outgoing radiation, Relative Humidity, Surface temperature (below the snow), Soil temperature at the depths 5, 10, 15, 20, 25, 30, 40, 50, 60, 65, 70, 75, 80, 90, 110, 140, 150 and 200 cm and Soil moisture (vol%) at depths 5, 10, 15, 20, 25, 30, 40, 50 and 80 cm. The data set consists of 719 unique locations in total, with data coverage varying between sites and over time. It is available in two versions: In the first 'raw' version, data was restructured and reformatted from each original source into a common format, but was not tested for quality. In the second, Quality Checked version, the data set has additionally been run through a five-module Quality Check involving 1) removing common error values, 2) evaluating physically impossible values, 3) outlier-detection and evaluation, 4) unit conversions and 5) evaluation of likely instrument and/or calibration artifacts. The code for import, normalization and quality check with optional modules is made available in addition to the data and metadata. When using the data, the original data source should always be cited (found in readme and metadata document). Abbreviations used: AWI: Alfred Wegener Institute, Potsdam; CALM: Circumpolar Active Layer Monitoring through Arctic Data Center; CEN/Nordicana: Center for Northern Studies, Canada; FMI: Finnish Meteorological Institute; GEM: Greenland Ecosystem Monitoring; IARC: International Arctic Research Center, Fairbanks, US; NGEE: Next Generation Ecosystem Experiments, Fairbanks, US; NMI: Norwegian Meteorological Institute; SILA/Nordicana: SILA Network at Center for Northern Studies, Canada; SMHI: Swedish Meteorological and Hydrological Institute; WMO: World Meteorological Organization via Meteostat.net; DMI: Danish Meteorological Institut; NOAA: National Oceanic and Atmospheric Administration Global Monitoring Laboratory.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.203

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.007

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 head0.095
GPT teacher head0.265
Teacher spread0.170 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2025
Admission routes1
Has abstractyes

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