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Record W2139220129 · doi:10.1002/hyp.10273

A new method of snowmelt sampling for water stable isotopes

2014· article· en· W2139220129 on OpenAlexaff
Daniele Penna, Mansoor Ahmad, S. J. Birks, Lhoussaine Bouchaou, Mihael Brenčič, S. Bilal Butt, Ladislav Holko, Ghulam Jeelani, Daniel Martínez, George Melikadze, James B. Shanley, Sergey Sokratov, Tricia Stadnyk, Atsuko Sugimoto, Polona Vreča

Bibliographic record

VenueHydrological Processes · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of ManitobaAlberta Innovates
FundersCHIST-ERAInternational Atomic Energy Agency
KeywordsSnowmeltSnowpackEnvironmental scienceSnowHydrology (agriculture)MeltwaterAtmospheric sciencesMeteorologyGeologyGeography

Abstract

fetched live from OpenAlex

Abstract We modified a passive capillary sampler (PCS) to collect snowmelt water for isotopic analysis. Past applications of PCSs have been to sample soil water, but the novel aspect of this study was the placement of the PCSs at the ground‐snowpack interface to collect snowmelt. We deployed arrays of PCSs at 11 sites in ten partner countries on five continents representing a range of climate and snow cover worldwide. The PCS reliably collected snowmelt at all sites and caused negligible evaporative fractionation effects in the samples. PCS is low‐cost, easy to install, and collects a representative integrated snowmelt sample throughout the melt season or at the melt event scale. Unlike snow cores, the PCS collects the water that would actually infiltrate the soil; thus, its isotopic composition is appropriate to use for tracing snowmelt water through the hydrologic cycle. The purpose of this Briefing is to show the potential advantages of PCSs and recommend guidelines for constructing and installing them based on our preliminary results from two snowmelt seasons. Copyright © 2014 John Wiley & Sons, Ltd.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.742
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.056
GPT teacher head0.284
Teacher spread0.228 · 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 designNot applicable
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

Citations36
Published2014
Admission routes1
Has abstractyes

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