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Record W2336330883 · doi:10.1089/omi.2016.0038

An Open Letter in Support of Transformative Biotechnology and Social Innovation: SANKO University Innovation Summit in Medicine and Integrative Biology, Gaziantep, Turkey, May 5–7, 2016

2016· letter· en· W2336330883 on OpenAlexaff
Eyüp İlker Saygılı, Alaa Abou-Zeid, Salih Murat Akkın, Eleni Aklillu, O Barlas, Alexander Borda‐Rodriguez, Filiz Aydoğan Boschele, Zafer Çetin, Enes Coşkun, Yavuz Çoşkun, Güner Dağlı, Türkan Uğur Dai, Collet Dandara, Türkay Dereli, Levent Elbeylı, László Endrényi, Can Polat Eyigün, Alexandros G. Georgakilas, Bircan Günbulut, Kıvanç Güngör, Asım Güzelbey, Can Hekim, Farah Huzair, Sabit Kimyon, Ümit Karakaş, Biaoyang Lin, Adrián LLerena, Collen Masimirembwa, Ruth McNally, Alper Mete, Peşvin Sancar, Sanjeeva Srivastava, Lotte Steuten, Oylum TANRIÖVER, David Tyfield, Volkan İhsan Töre, Deniz Vuruşkan, Wei Wang, Louise Warnich, Ambroise Wonkam, Yusuf Ziya Yıldırım, İsmet Yılmaz, Ahmet Sınav, Nezih Hekim

Bibliographic record

VenueOMICS A Journal of Integrative Biology · 2016
Typeletter
Languageen
FieldMedicine
TopicBiotechnology and Related Fields
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSummitTransformative learningSocial innovationBiotechnologyEngineering ethicsPolitical scienceEnvironmental ethicsBiologySociologyEngineeringPublic relationsGeography

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.005
metaresearch head score (Gemma)0.034
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.054
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.034
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.000
Science and technology studies0.0070.003
Scholarly communication0.0060.003
Open science0.0020.002
Research integrity0.0540.044
Insufficient payload (model declined to judge)0.0070.004

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 head0.026
GPT teacher head0.314
Teacher spread0.289 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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

Citations0
Published2016
Admission routes1
Has abstractno

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