MétaCan
Menu
Back to cohort
Record W2895600093 · doi:10.1080/10256016.2018.1526792

Nitrogen isotope fractionation factors (<i>α</i>) measured and estimated from the volatilisation of ammonia from water at pH 9.2 and pH 8.5

2018· article· en· W2895600093 on OpenAlexafffund
Eduardo Cejudo, Sherry L. Schiff

Bibliographic record

VenueIsotopes in Environmental and Health Studies · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGroundwater and Isotope Geochemistry
Canadian institutionsUniversity of Waterloo
FundersUniversity of WaterlooNatural Sciences and Engineering Research Council of CanadaConsejo Nacional de Ciencia y Tecnología
KeywordsFractionationEquilibrium fractionationIsotope fractionationChemistryIsotopeVolatilisationAmmoniaMass-independent fractionationStable isotope ratioIsotopes of nitrogenNitrogenKinetic isotope effectAnalytical Chemistry (journal)Environmental chemistryChromatographyDeuterium

Abstract

fetched live from OpenAlex

This paper examines the nitrogen isotope fractionation factors (α) associated with the volatilisation of ammonia from water under controlled conditions at two pH values (8.5 and 9.2). This experiment assumed the continuous removal of ammonia at a single purge rate of 10 mL air min−1. The fractionation resulting from the removal of total ammonia from the water into an acid trap was named the observed isotope fractionation factor (αobs), and it was measured as 1.019 (±0.0025) at pH 8.5 and 1.030 (±0.0025) at pH 9.2. The observed isotope fractionation factor includes the equilibrium isotope fractionation factor (αeq) and the kinetic isotope fractionation factor (αkin), each one mathematically derived from the experimental data. The equilibrium and kinetic isotope fractionation factors were estimated as αeq = 1.036 (±0.0014) and αkin = 1.050 (±0.003), respectively. Our results are compared to other previously measured and estimated fractionation factors.

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 score0.007
Threshold uncertainty score0.994

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 head0.041
GPT teacher head0.257
Teacher spread0.216 · 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

Citations11
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueIsotopes in Environmental and Health StudiesSame topicGroundwater and Isotope GeochemistryFrench-language works237,207