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Record W4288691577 · doi:10.1002/rcm.9370

Challenges in measuring nitrogen isotope signatures in inorganic nitrogen forms: An interlaboratory comparison of three common measurement approaches

2022· article· en· W4288691577 on OpenAlexaff
Christina Biasi, Simo Jokinen, Judith Prommer, Per Ambus, Peter Dörsch, Longfei Yu, S. J. Granger, Pascal Boeckx, Katja Van Nieuland, Nicolas Brüggemann, Holger Wissel, Andrey Voropaev, Tami Zilberman, Helena Jäntti, Tatiana Trubnikova, Nina Welti, Carolina Voigt, Beata Gebus‐Czupyt, Zbigniew Czupyt, Wolfgang Wanek

Bibliographic record

VenueRapid Communications in Mass Spectrometry · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsUniversité de Montréal
FundersEuropean Social FundEuropean Association of National Metrology Institutes
KeywordsChemistryNitrogenIsotopes of nitrogenEnvironmental chemistryAnalytical Chemistry (journal)Organic chemistry

Abstract

fetched live from OpenAlex

Rationale Stable isotope approaches are increasingly applied to better understand the cycling of inorganic nitrogen (N i ) forms, key limiting nutrients in terrestrial and aquatic ecosystems. A systematic comparison of the accuracy and precision of the most commonly used methods to analyze δ 15 N in NO 3 − and NH 4 + and interlaboratory comparison tests to evaluate the comparability of isotope results between laboratories are, however, still lacking. Methods Here, we conducted an interlaboratory comparison involving 10 European laboratories to compare different methods and laboratory performance to measure δ 15 N in NO 3 − and NH 4 + . The approaches tested were (a) microdiffusion (MD), (b) chemical conversion (CM), which transforms N i to either N 2 O (CM‐N 2 O) or N 2 (CM‐N 2 ), and (c) the denitrifier (DN) methods. Results The study showed that standards in their single forms were reasonably replicated by the different methods and laboratories, with laboratories applying CM‐N 2 O performing superior for both NO 3 − and NH 4 + , followed by DN. Laboratories using MD significantly underestimated the “true” values due to incomplete recovery and also those using CM‐N 2 showed issues with isotope fractionation. Most methods and laboratories underestimated the at% 15 N of N i of labeled standards in their single forms, but relative errors were within maximal 6% deviation from the real value and therefore acceptable. The results showed further that MD is strongly biased by nonspecificity. The results of the environmental samples were generally highly variable, with standard deviations (SD) of up to ± 8.4‰ for NO 3 − and ± 32.9‰ for NH 4 + ; SDs within laboratories were found to be considerably lower (on average 3.1‰). The variability could not be connected to any single factor but next to errors due to blank contamination, isotope normalization, and fractionation, and also matrix effects and analytical errors have to be considered. Conclusions The inconsistency among all methods and laboratories raises concern about reported δ 15 N values particularly from environmental samples.

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)0.121
metaresearch head score (Gemma)0.077
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.121
Threshold uncertainty score0.638

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1210.077
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.003
Science and technology studies0.0020.006
Scholarly communication0.0050.001
Open science0.0040.005
Research integrity0.0040.002
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 head0.120
GPT teacher head0.288
Teacher spread0.167 · 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 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

Citations13
Published2022
Admission routes1
Has abstractyes

Explore more

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