MétaCan
Menu
Back to cohort
Record W6893356729 · doi:10.5281/zenodo.15391938

Sifting for a Stream: The Morphology of the 300S Stellar Stream

2025· dataset· en· W6893356729 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2025
Typedataset
Languageen
FieldEngineering
TopicRobotics and Automated Systems
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSpline (mechanical)Node (physics)Morphology (biology)Subdivision

Abstract

fetched live from OpenAlex

This is the data associated with the paper Sifting for a Stream: The Morphology of the 300S Stellar Stream. We include: Machine readable tables of the spline node values with medians as well as 16% and 84% quantiles for each of the three models (the Sgr model and the 300S models from Methods 1 and 2). Machine readable tables of all spline node samples. These can be used to generate the splines using a natural cubic spline interpolator. In Python, this can be achieved through the "CubicSpline" object in the "scipy.interpolate" library using bc_type = 'natural'. Machine readable tables of the present day positions and velocities of the particles from the dynamical simulation. Further documentation and examples of loading the data can be found in the associated Jupyter notebooks.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.012
Threshold uncertainty score0.916

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.000
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 head0.020
GPT teacher head0.229
Teacher spread0.209 · 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