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Record W6906966872 · doi:10.18739/a2cp8h

Small Canadian Arctic River Flows

2015· dataset· en· W6906966872 on OpenAlexaboutno aff

Bibliographic record

VenueCalifornia Digital Library · 2015
Typedataset
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsArcticArchipelagoSubarctic climateBayArctic dipole anomalyDischargeThroughflowThe arctic

Abstract

fetched live from OpenAlex

The contribution of small Canadian Arctic rivers to the total freshwater flux through the Canadian Arctic Archipelago (CAA) is unknown and remains a significant gap in the growing data set addressing the freshwater budget of the Arctic and subarctic oceans. Limited geochemical data have been collected from the CAA, Nares Strait, and Baffin Bay, and the existing data sets are too sparse to differentiate among the various freshwater sources thought to contribute to the total freshwater pool (i.e., Pacific water, sea-ice meltwater, and meteoric water derived from the Mackenzie River, Eurasian rivers, and local runoff). New programs have begun to collect requisite geochemical data in Davis Strait and other regions. However, interpretation of these measurements may be biased by typical endmember assignments associated with Eurasian and North American river runoff. Characterizing the geochemical signature of local freshwater inputs is essential for distinguishing these contributions from those of Arctic Ocean origin, but virtually no studies have sampled small Arctic rivers discharging into the CAA. Available data collected from Baffin Bay and Hudson Strait suggest local rivers do not resemble the Mackenzie and Yukon Rivers typically assumed to represent North American runoff. While the annual discharge of any given river is relatively small, the combined discharge of all rivers is sufficient to support nearshore, narrow boundary currents, which provide an important, but often neglected, transport mechanism. Thus, local contributions of freshwater may impact the total volume flux and geochemistry of the Canadian Arctic throughflow that has historically been attributed entirely to export from the Arctic Ocean. The project consists of a three-year study to characterize the total alkalinity, barium, DOC, major ion and isotope (δ18O, 87Sr/86Sr) geochemistry of remote CAA rivers and estuaries with the ultimate aim of resolving the contribution of local freshwater inputs to CAA boundary currents. River sampling will occur during different flow regimes, but emphasize the spring freshet, the time of year when terrestrial runoff from local CAA rivers maximally impacts coastal waters. Estuarine sampling will include both horizontal and vertical profiles. The study focuses on eight rivers: the Coppermine, Ellice, Back, Hayes, Kuujuua, Thomsen, Cunningham, and Clyde Rivers. During summer field seasons of 2014-2016, four researchers are transported to each site via Twin Otter aircraft. Field operations begin at Kugluktuk (Coppermine River) in early July 2014 (river sampling only) and in early August of 2015 and 2016 (river and estuary sampling). Researchers spend 3-5 days at each site. The team utilizes airports located at Kugluktuk, Clyde River, and Ulukhaktok. More remote sites near the Thomsen, Ellice, Cunningham, and Back Rivers are accessed via equipping the Twin Otter with tundra tires. River water samples are collected by wading into the river and using an extendable pole to collect bulk (1L) samples from the central current. Bulk samples are then immediately filtered using a peristaltic pump and small subsamples are collected. Weekly samples are also collected by local workers in the more populated regions surrounding the Coppermine and Clyde Rivers to gain insight into the changes in river chemistry over the course of the spring and summer flow periods. Samples are collected from estuaries adjoining the river mouths from small, inflatable boats and a peristaltic pump equipped with multiple lengths of C-FLEX tubing.

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)0.004
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 score0.026
Threshold uncertainty score0.189

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0040.006
Science and technology studies0.0050.001
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0150.001

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 head0.014
GPT teacher head0.171
Teacher spread0.157 · 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
Published2015
Admission routes1
Has abstractyes

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