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Record W6950154750 · doi:10.5281/zenodo.6809291

Molar

2022· other· en· W6950154750 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) · 2022
Typeother
Languageen
FieldSocial Sciences
TopicCriminal Law and Policy
Canadian institutionsNatural Sciences and Engineering Research Council of CanadaMcGill UniversityUniversity of OttawaUniversity of Toronto
Fundersnot available
KeywordsFocus (optics)Python (programming language)Database applicationUser interfaceEvent (particle physics)Scripting language

Abstract

fetched live from OpenAlex

Molar is a database management system for PostgreSQL. Its main focus is to enable chemists and material scientist to store the results of their experiment, whether exprimental or not! It consists of a REST API (implemented using FastAPI) and a python client. ITs main features are: Creation / deletion of database on user request User management per database (using JWT tokens and OAuth2) Event sourcing to be sure not to lose any data Client integrates with PyData's pandas Support to have different database structure, thanks to Alembic Easy to deploy (you just need 2 command lines, <code>using docker-compose</code>)

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 categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.600
Threshold uncertainty score0.998

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 studies0.0040.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.6220.022

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 head0.053
GPT teacher head0.308
Teacher spread0.255 · 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