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Record W4394075521 · doi:10.6084/m9.figshare.3833310

Introduction to Ecology: A Study on Hair Colour and Gender

2016· dataset· en· W4394075521 on OpenAlex
Avani Abraham

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueFigshare · 2016
Typedataset
Languageen
FieldEngineering
TopicDyeing and Modifying Textile Fibers
Canadian institutionsnot available
Fundersnot available
KeywordsEcologyGeographyBiology

Abstract

fetched live from OpenAlex

Methods: A verbal survey was conducted using 22 human subjects to assess the relationship between hair colour and gender.<br><br>Study Site: The study was conducted in lab room 118 in Lubers building on the York University Keele Campus in Toronto, Ontario on September 15th, 2016. No equipment was used, data was collected by verbal survey of the subjects.<br><br>Hypothesis: There is no correlation between hair colour and gender because the genes that code for these two traits are not linked.<br><br>Predictions: <br>1) There will be no relationship between hair colour and gender as they are not genetically linked traits.<br><br>2) Males will not predominantly have one particular hair colour due to their gender, as the gene coding for hair colour does not reside on the X or Y chromosomes.<br><br>3) Females will not predominantly have one particular hair colour due to their gender (as a result of their lack of a Y chromosome), as the gene coding for hair colour does not reside on the X or Y chromosome.<br><br><br> <br>

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.020
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0260.006

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 head0.041
GPT teacher head0.268
Teacher spread0.227 · 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