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Record W2053369816 · doi:10.1016/j.jalz.2009.05.158

Creating a transatlantic research enterprise for preventing Alzheimer's disease

2009· article· en· W2053369816 on OpenAlexaff
Zaven S. Khachaturian, Jordi Camı́, Sandrine Andrieu, Jesús Ávila, Merçé Boada, Monique M.B. Breteler, Lutz Froelich, Serge Gauthier, Teresa Gómez‐Isla, Ara S. Khachaturian, Lewis H. Kuller, Eric B. Larson, Oscar L. López, J. M. Martínez‐Lage, Ronald C. Petersen, Gerard D. Schellenberg, Jordi Sunyer, Bruno Vellas, Lisa J. Bain

Bibliographic record

VenueAlzheimer s & Dementia · 2009
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsMcGill University
FundersNational Institute on Aging
KeywordsStandardizationOperabilityIdentification (biology)Key (lock)Political scienceEuropean unionBrain researchBusinessMedicineKnowledge managementPublic relationsEngineeringPsychologyComputer scienceComputer securityNeuroscienceInternational trade

Abstract

fetched live from OpenAlex

In recognition of the global problem posed by Alzheimer's disease and other dementias, an international think-tank meeting was convened by Biocat, the Pasqual Maragall Foundation, and the Lou Ruvo Brain Institute in February 2009. The meeting initiated the planning of a European Union-North American collaborative research enterprise to expedite the delay and ultimate prevention of dementing disorders. The key aim is to build parallel and complementary research infrastructure that will support international standardization and inter-operability among researchers in both continents. The meeting identified major challenges, opportunities for research resources and support, integration with ongoing efforts, and identification of key domains to influence the design and administration of the enterprise.

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)0.085
metaresearch head score (Gemma)0.031
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: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.085
Threshold uncertainty score0.450

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0850.031
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0070.004
Scholarly communication0.0130.009
Open science0.0020.019
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0080.003

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 head0.062
GPT teacher head0.384
Teacher spread0.322 · 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

Citations20
Published2009
Admission routes1
Has abstractyes

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