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Record W1985024313 · doi:10.1021/ed084p2024

OMLeT—An Alternative Approach to Learning Metabolism: Glycolysis and the TCA Cycle as an Example

2007· article· en· W1985024313 on OpenAlexaff
Michael G. Surette, Charles M. Stevens, Dylan M. Silver, Brad Behm, Raymond J. Turner

Bibliographic record

VenueJournal of Chemical Education · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics, Bioinformatics, and Biomedical Research
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCitationComputer scienceSocial mediaLibrary scienceWorld Wide Web

Abstract

fetched live from OpenAlex

The techniques and learning styles used by students to learn metabolism are as varied as the topics in biology. Given the technological advancement of computers, current resources (including textbook CDs and online Web sites) still fail to address the needs of all students, largely due to a psychological phenomenon called the hindsight bias. Personal experience expressed by undergraduates, verified the need for an additional resource, one that is interactive, simple, and informative. This resulted in the development of a Web site called OMLeT, an acronym for Online Metabolism Learning Tool. Through the use of PHP Hypertext Preprocessor (PHP) scripting, this project led to the creation of a dynamic Web site that is geared towards several aspects of learning styles and allows the student to process metabolic pathways via a user-defined approach. Furthermore, the versatility of the site provides the user with a comprehensive reference point. Currently, the site is constructed to contain two pathways, glycolysis and TCA cycle.

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 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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.117
Threshold uncertainty score0.253

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.018
GPT teacher head0.317
Teacher spread0.299 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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
Published2007
Admission routes1
Has abstractyes

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