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Record W2889661769

Applications of Noble Gases in Hydrogeology in Fractured, Fast Infiltration Systems - From the Greenland and Columbia Ice Sheets to Hawaii

2018· article· en· W2889661769 on OpenAlexaboutno aff
Yi Niu

Bibliographic record

VenueDeep Blue (University of Michigan) · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGroundwater and Isotope Geochemistry
Canadian institutionsnot available
Fundersnot available
KeywordsGeologyHydrogeologyInfiltration (HVAC)Noble gasGreenland ice sheetIce sheetGeomorphologyGeochemistryHydrology (agriculture)Geotechnical engineeringMeteorologyGeographyChemistry
DOInot available

Abstract

fetched live from OpenAlex

Due to their temperature dependency, stable noble gases (He, Ne, Ar, Kr, and Xe) have been routinely used as indicators of past climate in sedimentary systems for over four decades. However, noble gas studies in fractured systems, where infiltration is rapid, remain scarce. These include studies in ice sheets in old cratonic regions (e.g., Antarctica and Greenland), as well as old and recent volcanic areas such as the archipelagos of the Galapagos and Hawaii. Here, noble gas studies in fractured systems are presented. These include two studies in ice-covered regions, one in the Greenland Ice Sheet (GrIS) and the other in the Athabasca Glacier (AG) of the Columbia Icefield in the Canadian Rockies, as well as two studies in a tropical basaltic island, the Island of Maui, Hawaii. Noble gases in the GrIS (Chapter 2) and the AG (Chapter 3) studies are used to constrain glacial meltwater sources, water source altitude and water residence times. In Maui, noble gases are first used to characterize the different water sources contributing to groundwater recharge (e.g., fog, orographic and synoptic-scale rain), and to assess whether timing and location of recharge can be estimated based on atmospheric noble gas signatures (Chapter 4). In Chapter 5, the potential for noble gases to record temporal variations is assessed. Noble gases are used together with oxygen and hydrogen isotopic composition data to further constrain water source altitudes in Maui. Noble gases in the meltwater samples from both the GrIS and the AG are dominated by a partially equilibrated air-saturated water (ASW) component rather than trapped air in the glacial ice. Water source altitudes based on Xe range between 0.8 and 2.4 km for most samples from the GrIS and between 2.5 and 3.5 km for the AG. A crustal He component, observed in almost all samples in both studies, is used to estimate water residence times. Most meltwater samples from the GrIS yield water residence times between ~100 and ~400 years while two samples yield older ages of ~2000 and ~4000 years. In contrast, samples from the AG yield a younger average of ~160 years. Water samples were collected in Maui from rain events, springs from perched aquifers, and wells tapping the basal aquifer in June 2014 and February 2016. All samples are in disequilibrium with the atmosphere at the collection point and do not represent the mean annual air temperature. Distinct noble gas signatures in spring and basal aquifer samples suggest that the two types of aquifers are separate entities. In June 2014, noble gases in rainwater and basal aquifer display an ice-like signature possibly related to synoptic-scale rain. The basal aquifer yields similar noble gas signatures in both sampling seasons, while temporal variations are observed in rainwater and spring samples. A few springs and wells yield samples with a significant mantle He component in both years. A combined dataset of noble gas and water stable isotopic composition yield source altitudes for rainwater samples between 0.1 and 3 km above sea level (asl). Water source altitudes for most groundwater samples range between 1.5 and 5.5 km asl, indicating that the water source contributing to groundwater recharge that originate at higher altitudes in the atmosphere was not sampled. This dissertation has important scientific implications in the fields of glaciology, hydrogeology, and meteorology.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.747
Threshold uncertainty score0.889

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)0.0000.000

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 head0.004
GPT teacher head0.161
Teacher spread0.156 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2018
Admission routes1
Has abstractyes

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