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Record W2537385143 · doi:10.1016/j.jalz.2016.06.395

FTS2‐01‐05: From the who Research Priorities and Research Portfolio Analysis to the Identification of Research Gaps to Reduce the Global Burden of Dementia

2016· article· en· W2537385143 on OpenAlexaff
Hiral Shah, Emiliano Albanese, Cynthia Duggan, Igor Rudan, Kenneth M. Langa, María C. Carrillo, Kit Yee Chan, Yves Joanette, Martin Prince, Martin N. Rossor, Shekhar Saxena, Heather M. Snyder, Reisa A. Sperling, Mathew Varghese, Huali Wang, Marc Wortmann, Tarun Dua

Bibliographic record

VenueAlzheimer s & Dementia · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicHealth, Environment, Cognitive Aging
Canadian institutionsCanadian Institutes of Health Research
Fundersnot available
KeywordsDementiaIdentification (biology)PortfolioGerontologyPsychologyMedicineBusinessDiseaseFinancePathology

Abstract

fetched live from OpenAlex

This abstract is embargoed as part of the AAIC 2015 Featured News Program and will be released on Monday, July 25 after 8:00 AM. The full abstract content will be posted for your viewing after the embargo lifts. All abstracts will be published in Alzheimer's & Dementia: The Journal of the Alzheimer's Association.

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)0.051
metaresearch head score (Gemma)0.071
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.949
Threshold uncertainty score0.276

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0510.071
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0040.005
Science and technology studies0.0020.001
Scholarly communication0.0140.005
Open science0.0030.009
Research integrity0.0060.005
Insufficient payload (model declined to judge)0.0830.038

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 head0.087
GPT teacher head0.399
Teacher spread0.312 · 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
DomainMethods
GenreEmpirical

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

Citations1
Published2016
Admission routes1
Has abstractyes

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