Dataset with Facial Emotion Recognition Accuracy Scores and Polycystic Ovary Syndrome Symptoms in a Young Adult Sample
Bibliographic record
Abstract
This dataset includes the data reported on in the paper noted below. It includes data on 236 participants with 178 of these having data that was used to examine facial emotion recognition accuracy in the paper described below. It includes 126 participants assigned female at birth (AFAB) and 52 assigned male at birth (AMAB). Of the 126 AFAB, 19 met provisional criteria for a Polycystic Ovary Syndrome (PCOS) diagnosis and 107 AFAB individuals did not meet this criterion. Other variables include: adverse childhood experience scale scores, perceived stress scale scores, positive and negative affect schedule scores, demographics, and general health variables. All main variables necessary to reproduce the analyses reported in the manuscript are included in this dataset. Certain variables—such as current medications, college major, and specific ethnicity—were excluded to protect participant anonymity. Additionally, for a small number of participants, data that could potentially lead to identification were removed (e.g., extreme/unique height, weight, age, and gender). Variables ending with _X were created by the authors, and variables beginning with Imp_Corr_ reflect the raw FER data with imputed values to address missing values.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.024 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".