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Record W4389979419 · doi:10.24918/cs.2023.48

Where Does Elsie's Hair Color Come From? A "De-Simplified" Pedigree Lesson

2023· article· en· W4389979419 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.

Bibliographic record

VenueCourseSource · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicMedia, Gender, and Advertising
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsArt

Abstract

fetched live from OpenAlex

Pedigree analysis is part of most Genetics curricula, but the examples traditionally used in genetics courses present phenotypes as if they were entirely and inexorably defined by genotype. This does not reflect the current state of understanding in genetics, and can inadvertently reinforce the inaccurate belief that characteristics associated with any socially-defined group is governed by genes. In order for Genetics resources to better reflect present-day knowledge, instructors need teaching resources that acknowledge the multifactorial nature of phenotypic variation. Such resources are still scarce, particularly for pedigrees. This pedigree lesson, set up as a case study, allows students to &ldquo;discover&rdquo; the complexities of genotype-phenotype relationships using data from a published study. Students first become familiar with the specific single nucleotide polymorphism (SNP) in a single gene associated with the phenotype of interest (hair color), then contend with a series of increasingly challenging pedigrees, the last one seeming unsolvable. They then examine a figure from the research paper, showing the broad and overlapping ranges in hair color in each of the three relevant genotypic groups. This becomes the starting point for explaining the apparent inconsistencies in the most challenging pedigree, and for discussing the real-life complexities behind phenotypes and pedigree analysis. The lesson was well received by students, and their post-lesson assignments demonstrated a nuanced understanding of phenotype. Answers to exam assessment questions showed excellent pedigree analysis skills and a keen eye for the influence of environment on phenotypes. <em>Primary Image:</em>&nbsp;An artist&#39;s rendition of phenotypic variation vs. pedigree simplicity. A pedigree chart is superimposed onto an image of the head, face and neck of a person with colorful hair. The colorful hair is reduced to black or white in areas where the pedigree is superimposed. Artwork by <a href="http://jacelyndesigns.com">Jacelyn Shu</a>, used with the artist&rsquo;s permission.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.559
Threshold uncertainty score0.702

Codex and Gemma teacher scores by category

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

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 head0.038
GPT teacher head0.340
Teacher spread0.302 · 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