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Record W2974421804 · doi:10.1002/bmb.21302

Rethinking enzyme kinetics: Designing and developing a biomolecular interactive tutorial (BIOMINT) learning tool for undergraduate students

2019· article· en· W2974421804 on OpenAlex
Jerry Gu, Stavroula Andreopoulos, Jodie Jenkinson, Derek P. Ng

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

VenueBiochemistry and Molecular Biology Education · 2019
Typearticle
Languageen
FieldChemistry
TopicVarious Chemistry Research Topics
Canadian institutionsAmgen (Canada)University of Toronto
FundersUniversity of Toronto
KeywordsComputer scienceKineticsChemistryMultimediaHuman–computer interactionPhysics

Abstract

fetched live from OpenAlex

Enzyme kinetics is the study of enzymatic catalytic rates in biochemical reactions. This topic is commonly taught to life science students in introductory biochemistry courses during their undergraduate education. Unlike most other biochemistry topics, which focus on visual structures of biomolecules and their processes, enzyme kinetics is explained primarily through abstract mathematical and two-dimensional graphical plots. However, these abstract/symbolic representations often make it difficult for students to relate the kinetic parameters to the underlying molecular system that is being described. In this article, we present the design and development of a web-based multimedia interactive learning tool, biomolecular interactive tutorials (BIOMINT) to help students better bridge the relationships between these abstract mathematical models and the molecular behaviors, interactions, and dynamics that produce kinetic phenomena. This learning tool can be accessed at https://bit.ly/biomint. © 2019 International Union of Biochemistry and Molecular Biology, 48(1):74-79, 2020.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.004
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.012
GPT teacher head0.320
Teacher spread0.308 · 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