MétaCan
Menu
← Back to cohort
Record W7111278786 · doi:10.5281/zenodo.16994852

A complex structure of escaping helium spanning more than half the orbit of the ultra-hot Jupiter WASP-121b

2025· dataset· W7111278786 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2025
Typedataset
Language
Field
Topic
Canadian institutionsHerzberg Institute of AstrophysicsMcGill UniversityUniversité de Montréal
Fundersnot available
KeywordsExoplanetHeliumHot JupiterSpectrographSatellitePlanetAtmosphere of JupiterInfraredJupiter (rocket family)

Abstract

fetched live from OpenAlex

This dataset contains the full-orbit helium phase-curve observations of the ultra-hot Jupiter WASP-121 b obtained with the James Webb Space Telescope (JWST) Near Infrared Imager and Slitless Spectrograph (NIRISS). The Python scripts used to produce the figures shown in the paper are provided with a README file, the extracted data from NAMELESS and exoTEDRF, the best-fit EVE toy-model. The data include flux measurements, wavelength calibrations, and associated uncertainties, capturing helium absorption over nearly 60% of the planetary orbit. These observations reveal a strong atmospheric outflow forming two distinct tails with different kinematics and provide critical constraints on hydrodynamic escape in highly irradiated exoplanets. The dataset supports analyses of mass-loss rates, atmospheric dynamics, and comparisons with theoretical models. It is intended for use in exoplanet atmosphere studies, model validation, and educational purposes.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.018
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0160.023

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 head0.036
GPT teacher head0.268
Teacher spread0.232 · 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 designNot applicable
Domainnot available
GenreDataset

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 venueZenodo (CERN European Organization for Nuclear Research)→French-language works237,207→