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Record W1965792262 · doi:10.1021/ac000498n

An Automated Technique for Measuring δD and δ<sup>18</sup>O Values of Porewater by Direct CO<sub>2</sub> and H<sub>2</sub> Equilibration

2000· article· en· W1965792262 on OpenAlexaff
Geoff Koehler, Leonard I. Wassenaar, M. Jim Hendry

Bibliographic record

VenueAnalytical Chemistry · 2000
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGroundwater and Isotope Geochemistry
Canadian institutionsUniversity of SaskatchewanEnvironment and Climate Change Canada
Fundersnot available
KeywordsChemistryExtraction (chemistry)FractionationBoreholeAnalytical Chemistry (journal)Environmental chemistryChromatographyGeology

Abstract

fetched live from OpenAlex

The stable-oxygen and -hydrogen isotopic values (deltaD, delta18O) of porewater in geologic media are commonly determined on water obtained by extraction techniques such as centrifugation, mechanical squeezing, vacuum heating and cryogenic microdistillation, and azeotropic distillation. Each of these techniques may cause isotopic fractionation as part the extraction process and each is laborious. Here we demonstrate a new approach to obtain automated, high-precision deltaD and delta18O measurements of porewater in geologic sediments by direct H2- and CO2-porewater equilibration using a modified commercial CO2-water equilibrator. This technique provides an important and cost-effective improvement over current extraction methods, because many samples can be rapidly analyzed with minimal handling, thereby reducing errors and potential for isotopic fractionation. The precision and accuracy of direct H2- and CO2-porewater equilibration is comparable to or better than current porewater extraction methods. Finally, the direct equilibration technique allows investigators to obtain high-resolution (cm scale) porewater deltaD and delta18O profiles using cores from individual boreholes, eliminating the need for costly piezometers or conventional porewater extractions.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.071
Threshold uncertainty score0.992

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 head0.011
GPT teacher head0.228
Teacher spread0.217 · 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 designBench or experimental
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

Citations48
Published2000
Admission routes1
Has abstractyes

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