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Record W2943524929 · doi:10.1126/science.aax7509

Consent insufficient for data release—Response

2019· letter· en· W2943524929 on OpenAlexaff
Rudolf Amann, Shakuntala Baichoo, Benjamin J. Blencowe, Peer Bork, Mark Borodovsky, Cath Brooksbank, Patrick Chain, Rita R. Colwell, Daniele Daffonchio, Antoine Danchin, Vı́ctor de Lorenzo, Pieter C. Dorrestein, ROBERT FINN, Claire M. Fraser, Jack A. Gilbert, Steven Hallam, Philip Hugenholtz, John P. A. Ioannidis, Janet Jansson, Jihyun F. Kim, Hans-Peter Klenk, Martin G. Klotz, Rob Knight, Konstantinos Konstantinidis, Nikos C. Kyrpides, Christopher E. Mason, Alice C. McHardy, Folker Meyer, Christos Ouzounis, A.A.N. Patrinos, Mircea Podar, Katherine S. Pollard, Jacques Ravel, Alejandro Reyes, Richard J. Roberts, Ramon Rosselló‐Móra, Susanna‐Assunta Sansone, Patrick D. Schloss, Lynn M. Schriml, João Carlos Setúbal, Rotem Sorek, Rick Stevens, James M. Tiedje, Adrián G. Turjanski, Gene W. Tyson, David W. Ussery, George M. Weinstock, Owen White, William B. Whitman, Ioannis Xénarios

Bibliographic record

VenueScience · 2019
Typeletter
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsUniversity of British ColumbiaUniversity of Toronto
FundersBiotechnology and Biological Sciences Research Council
KeywordsConfidentialityOpenness to experienceInternet privacyData sharingTransparency (behavior)Public relationsBusinessData collectionPolitical sciencePsychologyComputer scienceSociologyLawMedicineSocial psychology

Abstract

fetched live from OpenAlex

Nicol et al. make discerning comments on issues related to data sharing and ethics, regulations, and imbalance of power. We broadly share their concerns, and we did indeed mention some of them briefly in our Policy Forum. Our discussion pertained to all types of data, many or most of which do not include any personal information or do not involve individuals or humans in any way.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.072
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.930
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.072
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0060.004
Scholarly communication0.0040.005
Open science0.0040.004
Research integrity0.0700.054
Insufficient payload (model declined to judge)0.0090.007

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 head0.803
GPT teacher head0.651
Teacher spread0.152 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreEditorial

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".

Quick stats

Citations5
Published2019
Admission routes1
Has abstractyes

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