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Record W3082823622 · doi:10.1128/jmbe.v21i2.2211

The Course “Microbes and You”: A Concrete Example that Addresses the Urgent Need for Microbiology Literacy in Society

2020· article· en· W3082823622 on OpenAlexaff
Luc Trudel, Hélène Deveau, Cynthia Gagné-Thivierge, Steve J. Charette

Bibliographic record

VenueJournal of Microbiology and Biology Education · 2020
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAlexander von Humboldt Studies
Canadian institutionsInstitut universitaire de cardiologie et de pneumologie de QuébecUniversité Laval
Fundersnot available
KeywordsCourse (navigation)MicrobiologySet (abstract data type)Scientific literacyComputer scienceMedical educationMedicineMathematics educationBiologyEngineeringPsychologyScience education

Abstract

fetched live from OpenAlex

Microbiology literacy is essential for any world citizen, considering the major impacts that microorganisms have on our daily lives and health. Consequently, we have created a complete microbiology course available to the non-microbiology students at our university. This course, entitled "Microbes and You" is fully accessible online (in French only) and helps students advance their knowledge of microorganisms in a fun way. Before the course, most students are only aware of the negative role of microorganisms on our health, and this course teaches them that our relationship with microbes is more complex and possesses many advantages. In this article, we share our experience and the extremely positive response of the students. We hope to encourage other microbiologists to set up basic courses in microbiology for non-scientists, or scientists without basic knowledge in this science.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.003
Scholarly communication0.0030.002
Open science0.0010.005
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0140.003

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 head0.046
GPT teacher head0.307
Teacher spread0.260 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations8
Published2020
Admission routes1
Has abstractyes

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