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Chemspyd: An Open-Source Python Interface for Chemspeed Robotic Chemistry and Materials Platforms

2024· preprint· en· W4391969161 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueChemRxiv · 2024
Typepreprint
Languageen
FieldEngineering
TopicRobotics and Automated Systems
Canadian institutionsCanadian Institute for Advanced ResearchVector InstituteUniversity of Toronto
FundersNatural Resources CanadaAdvanced Research Projects AgencyDefense Advanced Research Projects Agency
KeywordsPython (programming language)WorkflowSuiteComputer scienceModular designOpen sourceSoftware engineeringInterface (matter)SoftwareSoftware suiteScripting languageOpen source softwareUser interfaceProgramming languageOperating systemDatabase

Abstract

fetched live from OpenAlex

We introduce Chemspyd, a lightweight, open-source Python package for operating the popular laboratory robotic platforms from Chemspeed Technologies. As an add-on to the existing proprietary software suite, Chemspyd enables dynamic communication with the automated platform, laying the foundation for its modular integration into customizable, higher-level laboratory workflows. We show the applicability of Chemspyd in a set of case studies from chemistry and materials science. We demonstrate how the package can be used with large language models to provide a natural language interface. By providing an open-source software interface for a commercial robotic platform, we hope to inspire the development of open interfaces that facilitate the flexible, adaptive integration of existing laboratory equipment into automated laboratories.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.112
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.025
GPT teacher head0.267
Teacher spread0.243 · 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