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Record W3190241539 · doi:10.1594/pangaea.925392

Physical properties and stable isotope composition of snow in the Canadian Arctic tundra and subarctic taiga (Spring-Summer 2018, 2019)

2020· dataset· en· W3190241539 on OpenAlexaboutno aff
Simon Levasseur, Kristina A. Brown, Alexandre Langlois, Donald McLennan

Bibliographic record

VenuePublishing Network for Geoscientific and Environmental Data (PANGAEA) (Alfred Wegener Institute for Polar and Marine Research) · 2020
Typedataset
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsnot available
Fundersnot available
KeywordsSubarctic climateTundraTaigaSpring (device)SnowArcticEnvironmental sciencePhysical geographySnowmeltComposition (language)BorealOceanographyClimatologyEcologyGeographyForestryGeologyMeteorologyBiology

Abstract

fetched live from OpenAlex

This data set describes snow physical and geochemical observations collected in the Canadian Arctic between March 2018 and June 2019. Snow samples were collected as part of the project entitled "Development of a multi-scale cryosphere monitoring network for the Kitikmeot region and Northwest territories using in-situ measurements, modeling and remote sensing" led by Dr. Alex Langlois, Université de Sherbrooke. Sampling was carried out at two primary locations in the Canadian sub-Arctic and Arctic: Wekweètì (NWT), located just south of tree line, and Greiner Lake Watershed (NU), situated well into Arctic tundra on Victoria Island (near Cambridge Bay, NU). Data are also included from Herschel Island (NWT) and Trail Valley Creek (NWT). Sampling was conducted primarily from spring into summer, with snow samples collected in Wekweètì over the month of March 2018 and Cambridge Bay over the months of April and July 2018, and April to June 2019. Trail Valley Creek was visited in winter, January 2019, whereas Herschel Island samples were collected in April and May 2019. Snow physical properties were measured following the methods outlined in Levasseur et al., (submitted). Briefly, snowpits were excavated along transects to conduct observations of snow stratigraphy, density, temperature, grain size, and grain type following Langlois et al., (2009). Density profiles were measured by extracting snow samples at 3 cm intervals using 192 cm3 and 100 cm3 density cutters. The samples were weighed using a Pesola light series scale (100 g) from which density was calculated. Temperature profiles were also measured at 3 cm intervals using a digital temperature probe (+/- 0.1°C). Snow Water Equivalent was also determined for some layers after Langlois et al., (2009). Snow geochemical properties were also determined following the methods outlined in Levasseur et al., (submitted). Briefly, snow was collected into 1 L HDPE plastic snow containers using a clean plastic trowel. Snow samples were melted at room and/or fridge temperature, with melt progression checked at regular intervals. Once melted, samples were filtered through 0.22 μm Sterivex-GV filters into Wheaton 4 mL Amber Vials with TFE-Lined Caps for the analysis of stable isotope composition (δ18O-H2O and δ2H-H2O). For samples collected in 2018, stable isotope analyses were conducted at the Environmental Chemistry Facility at Brown University (RI) using a Picarro L1102-i Isotopic Water Liquid Analyzer with a standard error of +/- 0.1 ‰ for δ18O-H2O and +/-1 ‰ for δ2H-H2O. For samples collected in 2019, stable isotope analyses were conducted at the University of Calgary using a Los Gatos Research Liquid Water Isotope Analyzer with a reported analytical precision of ± 0.2‰ for δ18O-H2O and ± 2‰ for δ2H-H2O.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.086
GPT teacher head0.255
Teacher spread0.169 · 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 designObservational
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

Citations2
Published2020
Admission routes1
Has abstractyes

Explore more

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