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Record W2118677714 · doi:10.1187/cbe.06-03-0152

Enhancing Undergraduate Teaching and Research with a<i>Drosophila</i>Virginizing System

2006· article· en· W2118677714 on OpenAlexafffund
Dennis R. Venema

Bibliographic record

VenueCBE—Life Sciences Education · 2006
Typearticle
Languageen
FieldNeuroscience
TopicOlfactory and Sensory Function Studies
Canadian institutionsTrinity Western UniversityWestern University
FundersTrinity Western University
KeywordsDrosophila (subgenus)Mathematics educationBiologyPsychologyGenetics

Abstract

fetched live from OpenAlex

Laboratory exercises using Drosophila crosses are an effective pedagogical method to complement traditional lecture and textbook presentations of genetics. Undergraduate thesis research is another common setting for using Drosophila. A significant barrier to using Drosophila for undergraduate teaching or research is the time and skill required to accurately collect virgins for use in controlled crosses. Erroneously collecting males or nonvirgin females contaminates crosses with unintended genotypes and confounds the results. Collecting adequate numbers of virgins requires large amounts of time, even for those skilled in virgin collection. I have adapted an effective method for virgin collection that eliminates these concerns and is straightforward to use in undergraduate settings. Using a heat-shock-induced, conditional lethal transgene specifically in males, male larvae can be eliminated from a culture before adults eclose. Females thus eclose in the absence of males and remain virgin, eliminating the need to laboriously score and segregate freshly eclosed females. This method is reliable, easily adaptable to any desired phenotypic marker, and readily scaleable to provide sufficient virgins for large laboratory classes or undergraduate research projects. In addition, it allows instructors lacking Drosophila expertise to use this organism as a pedagogical tool.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.337
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.001
Scholarly communication0.0000.001
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 head0.150
GPT teacher head0.344
Teacher spread0.194 · 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.

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

Citations8
Published2006
Admission routes2
Has abstractyes

Explore more

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