Evidence map›Paper›PMID 41210741›Full record

ArticleACS omega2025

CO

Madeleine Cochrane, Sarah K Scholten, Chris Perrella, Anton van den Hengel, Kishan Dholakia, Andre N Luiten, Zhibin Liao, Johan W Verjans

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in ACS omega, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Madeleine CochraneAustralian Institute for Machine Learning (AIML), School of Computer and Mathematical Sciences, University of Adelaide, Adelaide, South Australia 5005, Australia.ORCID https://orcid.org/0000-0002-0986-322X
Sarah K ScholtenInstitute for Photonics and Advanced Sensing, School of Physics, Chemistry and Earth Sciences, University of Adelaide, Adelaide, South Australia 5005, Australia.
Chris PerrellaInstitute for Photonics and Advanced Sensing, School of Physics, Chemistry and Earth Sciences, University of Adelaide, Adelaide, South Australia 5005, Australia.
Anton van den HengelAustralian Institute for Machine Learning (AIML), School of Computer and Mathematical Sciences, University of Adelaide, Adelaide, South Australia 5005, Australia.
Kishan DholakiaInstitute for Photonics and Advanced Sensing, School of Physics, Chemistry and Earth Sciences, University of Adelaide, Adelaide, South Australia 5005, Australia.
Andre N LuitenInstitute for Photonics and Advanced Sensing, School of Physics, Chemistry and Earth Sciences, University of Adelaide, Adelaide, South Australia 5005, Australia.
Zhibin LiaoAustralian Institute for Machine Learning (AIML), School of Computer and Mathematical Sciences, University of Adelaide, Adelaide, South Australia 5005, Australia.
Johan W VerjansAustralian Institute for Machine Learning (AIML), School of Computer and Mathematical Sciences, University of Adelaide, Adelaide, South Australia 5005, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The estimation of molecular concentrations in gas mixtures has applications in healthcare, environmental monitoring, and food quality assessment. Direct frequency comb spectroscopy provides benefits as a sensing platform in these application areas due to its accuracy, species selectivity and capacity for broadband analysis and miniaturization. One challenge associated with direct frequency comb spectroscopy is the accurate estimation of the concentration levels of molecules with overlapping absorption features. Traditional analysis methods, such as curve fitting, do not scale well to mixtures containing many different molecules. Here, we use machine learning methods to estimate the concentration level of two isotopologues of carbon dioxide:

Identifiers

PMID41210741
PMCPMC12594007

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.