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LIDI 2 – New Evaluation Strategy for Accurate and Precise Clumped Isotope CO2 Analysis on Carbonate Samples

2020· article· en· W3082849005 on OpenAlexaff
Magda Mandić, Mario Tuthorn, Nils Stoebener, Jens Radke, Johannes Schwieters

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsThermo Fisher Scientific (Canada)
Fundersnot available
KeywordsIsotopeChemistrySample (material)Stable isotope ratioAnalytical Chemistry (journal)ChromatographyPhysics

Abstract

fetched live from OpenAlex

Long Integration Dual Inlet (LIDI) is an established technology which enabled improved accuracy and precision of Δ47 analysis from carbonate samples by utilizing sequential measurement of the full sample and reference, rather than alternating between sample and reference on shorter time periods, as it is done in the classical Dual Inlet method. As of today, there are two key challenges that were limiting further improvements to Δ47 determination: the IRMS must be in a stable temperature environment during long measurement of sample and reference gas, and the crimping of the sample and reference capillaries must be precisely matched, otherwise the produced data will be inaccurate and have reduced precision. Here we present the improvements made on the sample gas measurement and data evaluation, which we define as LIDI 2. By applying the LIDI 2 method, sample bracketing is possible following a four-step approach, resulting in fully corrected temperature drift (i.e. eliminated from the data), decreasing the standard deviation by factor of 2. This is a substantial improvement for acquiring clumped isotope data as reaching a very stable temperature of ±0.1°C/h is a challenge for most laboratories. Alongside eliminating variation in the Δ47 data caused by unstable laboratory air temperature, LIDI 2 also improves the overlap of sample and reference gas signals due to non-perfect crimping of the capillaries. The crimping procedure is laborious and rarely delivers perfect results. Additionally, the pressure adjustment before reference measurement must ensure there is no significant offset between sample and reference intensities. LIDI 2 delivers perfect sample versus reference intensity matching, which results in significantly higher precision on each sample gas analyzed. Standard error of a single sample measurement is improved by up to factor of 2. The LIDI 2 method delivers improved accuracy and precision on Δ47 measurement from small Carbonate samples, which in combination with the latest advancements in inert capillaries coating and automated contaminant trapping contributes to enhanced clumped isotopes data quality.

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)0.004
metaresearch head score (Gemma)0.003
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: none
Teacher disagreement score0.004
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.003

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 head0.062
GPT teacher head0.288
Teacher spread0.226 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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