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Record W4205145271 · doi:10.1111/bor.12577

GlaciDat – a GIS database of submarine glacial landforms and sediments in the Arctic

2022· article· en· W4205145271 on OpenAlexaboutno aff
Katharina Streuff, Colm Ó Cofaigh, Paul Wintersteller

Bibliographic record

VenueBoreas · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsnot available
Fundersnot available
KeywordsMoraineGeologyGlacial landformGlacial periodGlacierDrumlinLandformPaleontologyDeglaciationGeomorphologyRadiocarbon datingOceanographyIce streamSea iceCryosphere

Abstract

fetched live from OpenAlex

A digital database for submarine glacial landforms and sediments formed in the Arctic during and since the Last Glacial Maximum was created in order to facilitate and underpin new research on palaeo‐ice sheets and tidewater glacier dynamics. The glacimarine database (GlaciDat) documents and standardises evidence of previous glacial activity as visible on the contemporary seafloor of fjords and continental shelves around Svalbard, Greenland, Alaska, northern Russia and north of 66°30′N in Canada and Norway. An extensive literature search was conducted to create GlaciDat, which compiles nearly 60 000 individual submarine landforms, more than 1000 sediment cores and 232 radiocarbon dates. Glacial landforms included are cross‐shelf troughs, trough‐mouth fans, grounding‐zone wedges, lateral moraines, overridden moraines, (mega‐scale) glacial lineations, drumlins, crag‐and‐tails, medial moraines, terminal moraines, debris‐flow lobes (including glacier‐contact fans), recessional moraines, De Geer moraines, crevasse‐fill ridges, eskers, hill‐hole pairs, crescentic scours, and submarine channels. They were digitised as point, line and polygon features alongside a list of their individual characteristics. Sediment core locations are attributed with a description of the sampled lithofacies and sedimentation rates where available. Landforms and sediments have been standardised according to predefined nomenclatures to make the glacial evidence as consistent as possible. Marine radiocarbon dates were included when thought to be relevant for constraining the timing of large‐scale palaeo‐ice dynamics. Outlines of bathymetric data sets, which have previously been used for glacial geomorphological mapping, were also included to give an overview of already investigated research areas. GlaciDat is available for download ( https://doi.pangaea.de/10.1594/PANGAEA.937782 ) and will aid researchers in the reconstruction of past ice dynamics and the interpretation of Arctic glacial landform–sediment assemblages. Moreover, as well as providing a comprehensive bibliography on Arctic glacial geomorphological and sedimentological research, it is intended to serve as a basis for future modelling of Arctic glacier and ice‐sheet dynamics.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.244
Teacher spread0.226 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations15
Published2022
Admission routes1
Has abstractyes

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