Physical properties and stable isotope composition of rain, river, lake, and groundwater samples in the Canadian Arctic tundra and subarctic taiga (Summer 2018 and 2019)
Bibliographic record
Abstract
This data set describes surface water and late season snow melt physical and geochemical observations collected around the Greiner Lake Watershed (near Cambridge Bay, NU) over July 2018 and between April and June 2019, as well as several rain, river, lake, and groundwater samples collected opportunistically. Snow and surface water 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. Snow 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. Snow temperature was determined using a digital temperature probe (+/- 0.1°C). Surface water and late season snow melt 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). Rain samples were collected using a funnel rain gauge at the Canadian High Arctic Research Station, whereas lake and pond samples were collected from surface waters at the shore or edge, respectively. Soil and pore water samples were collected by digging a hole, filling containers and pressing their contents through a filter with a pestle. All water samples were then processed identically to the snow melt samples. River water samples were also collected from Freshwater Creek (69.131°N, -104.991°E), which directly drains Greiner Lake. Surface water samples for the determination of stable water isotopes were collected according to methods developed by the Arctic Great Rivers Observatory (Arctic-GRO; http://www.arcticgreatrivers.org/) as described in detail by Brown et al., 2020. 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".