CINECA synthetic cohort NA Canada CHILD [CC-BY-NC-SA]
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.
Bibliographic record
Abstract
The "CINECA synthetic cohort NA Canada CHILD" dataset is a synthetic dataset developed to provide insight into how data is structured for select common attributes in the CHILD Cohort Study, but not reveal any personal or identifiable information associated with cohort participants. Such synthetic datasets are valuable for software developers to be able to see specific examples of data for common attributes (i.e. a minimal metadata model of a selection of common variables usually present in cohorts). This dataset comprises 100 variables for 150 synthetic participants which have faked phenotypic data that reflects CHILD cohort data. In addition, there is genetic data based on the 1000 Genomes project. This dataset was created within the context of the CINECA project. More information about the creation of this dataset can be found in the included documentation. \n\n\nPlease note this preamble must be included with any distribution of this dataset: This synthetic dataset (with cohort “participants” / ”subjects” marked with FAKE) has no identifiable data and cannot be used to make any inference about CHILD cohort data or results. The purpose of this dataset is to aid development of technical implementations for cohort data discovery, harmonization, access, and federated analysis. In support of FAIRness in data sharing, this dataset is made freely available under the Creative Commons Licence (CC-BY; https://creativecommons.org/licenses/by-nc-sa/4.0/). Please ensure this preamble is included with this dataset and that the CHILD project and the CINECA project (funding: EC H2020 grant 825775 and CIHR grant 404896) are acknowledged. If you have any questions about this dataset contact Fiona Brinkman at brinkman@sfu.ca or Erin Gill at egill@sfu.ca.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.149 | 0.005 |
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 it