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Record W6924840034 · doi:10.1594/pangaea.932598

Surface topography and snow depth measured during the ALERT2018 campaign (MAP Last Ice) at station ALERT2018_12

2021· dataset· en· W6924840034 on OpenAlexaboutno aff

Bibliographic record

VenuePublishing Network for Geoscientific and Environmental Data (PANGAEA) (Alfred Wegener Institute for Polar and Marine Research) · 2021
Typedataset
Languageen
FieldMedicine
TopicCardiac tumors and thrombi
Canadian institutionsnot available
Fundersnot available
KeywordsScannerLaserSnowLaser scanningStandard deviationWavelength

Abstract

fetched live from OpenAlex

Measurements of surface topography have been performed using a Terrestrial Laser Scanner (VZ-400i, RIEGL, Horn, Austria) on First-Year-Ice during the ALERT2018 campaign (Multidisciplinary Arctic Program (MAP) - Last Ice) off Alert, Nunavut, Canada in the Lincoln Sea in May 2018. The scans were performed across an approx. 100 times 100 m wide patch. The laser scanner was mounted on a tripod approx. 2 m above the surface and had a wavelength of 1550 nm. The scan pattern “panorama 20” with an angular resolution of 0.02 degree and a scan time of 180 s was used. At 20 m this corresponds to a 0.7 cm and at 50 m to a 1.7 cm horizontal resolution. A laser pulse repetition rate of 1200 kHz with a maximum measurement range of 250 m was used. Due to the snow conditions and the laser wavelength, a maximum range of only about 100 m was achieved. The roll and pitch accuracy of the laser scanner ranged from 0.009 to 0.014 degree which translates into a 1.57 cm to 2.44 cm vertical error per 100 m distance. For further interpretation we used the mean of 2 cm for the laser scanner accuracy. The individual scans were each registered to a master scan position using three space-fixed cylindrical retro-reflectors (10 cm diameter) in the RiSCAN Pro software. The standard deviation of the reflectors calculated during the registration was between 0.5 cm and as much as 2.2 cm when the laser scanner was not entirely stable during windy conditions. Snow fall, moving targets, and the area outside the area of interest were manually removed in the RiSCAN Pro software. Surveys conducted during larger snow fall events were not further analysed. After registration, a full point cloud was created with mean horizontal resolutions of 5 cm. The snow depth was calculated from the surface topography using the length of the rod where the reflector sitting on level ice was mounted, the distance between rod end and reflector centre, and the z-coordinate of the reflector as measured by the laser scanner.

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)0.000
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.036
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.051
GPT teacher head0.286
Teacher spread0.236 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

Same venuePublishing Network for Geoscientific and Environmental Data (PANGAEA) (Alfred Wegener Institute for Polar and Marine Research)Same topicCardiac tumors and thrombiFrench-language works237,207