MétaCan
Menu
Back to cohort
Record W4408259295 · doi:10.1016/j.jqsrt.2025.109399

MARVEL analysis of high-resolution spectra of ozone (16O3)

2025· article· en· W4408259295 on OpenAlexaff
Apoorva Upadhyay, Tibor Furtenbacher, Armando N. Perri, Charles A Bowesman, E. K. Conway, K. L. Chubb, A. Owens, Caitlin P. Dobney, Ella M. Bowen, Daniel Broner, Victor Ciobanu, Katherina Gelborova, Sam Livsey, Damilola Magbagbeola, Madhushree Manjunatha, David Morohunfola, Emaan Wijayakoon, Sophie Winter, Jonathan Tennyson

Bibliographic record

VenueJournal of Quantitative Spectroscopy and Radiative Transfer · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsUniversity of Toronto
FundersNatural Environment Research CouncilUniversity College London
KeywordsOzoneHigh resolutionSpectral lineEnvironmental sciencePhysicsRemote sensingGeographyMeteorologyAstronomy

Abstract

fetched live from OpenAlex

Accurate rotation-vibration (ro-vibrational) energy levels of the main isotopologue of ozone ( 16 O 3 ) in its ground electronic X ̃ 1 A 1 state are determined by performing MARVEL (Measured Active Rotation Vibration Energy Levels) analysis on measured line positions from 29 sources of data published in scientific literature. A total of 50276 ro-vibrational transitions are considered yielding 13664 MARVEL energy levels of 16 O 3 with their associated uncertainties. The maximum values of the energy and rotational angular momentum quantum number in the MARVEL dataset are 8167.33 cm −1 and J = 67 respectively. Variational nuclear motion calculations of 16 O 3 energy levels are performed and subsequently used to assess and validate the MARVEL results. Comparisons with alternative data compilations based on effective Hamiltonians are also shown.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.014
GPT teacher head0.272
Teacher spread0.258 · 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 designSimulation or modeling
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

Citations1
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Quantitative Spectroscopy and Radiative TransferSame topicAtmospheric Ozone and ClimateFrench-language works237,207