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Record W2015094446 · doi:10.1159/000357527

Population Biobanking and International Collaboration

2014· article· en· W2015094446 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

VenuePathobiology · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Systems and Reforms
Canadian institutionsMcGill University
Fundersnot available
KeywordsBiobankPopulationBusinessPolitical scienceEnvironmental planningGeographyMedicineEnvironmental healthBiologyBioinformatics

Abstract

fetched live from OpenAlex

Population-based biobanks promise to be important resources for genetic research. However, the study of normal genomic variation across populations requires the collection of data and biological samples from individuals on a large scale. While international collaboration has become both a scientific and an ethical imperative, international sharing of data and samples poses many challenges. Significant variation persists among the legal and ethical norms governing population biobanks in different jurisdictions. Many of these norms do not clearly provide for international access. To illustrate these problems, we collected and compared applicable legislative instruments, as well as ethical guidelines issued by national, regional, and international bodies. In addition, harmonization is faced with important limitations and may not be sufficient to ensure effective international sharing. Population biobanks are therefore looking for new ways to promote sharing and improve interoperability. The formation of biobank networks and the development of common governance tools are two approaches that are setting the groundwork for international collaboration in genetic research.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.183
Threshold uncertainty score0.152

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)0.0000.000

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 head0.025
GPT teacher head0.252
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