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Record W2417185622 · doi:10.1016/j.cortex.2016.05.014

Reading words and other people: A comparison of exception word, familiar face and affect processing in the left and right temporal variants of primary progressive aphasia

2016· article· en· W2417185622 on OpenAlexfundno aff
Richard J. Binney, Maya L. Henry, Miranda Babiak, Peter Pressman, Miguel Santos‐Santos, Jared Narvid, Maria Luisa Mandelli, Paul J. Strain, Bruce L. Miller, Katherine P. Rankin, Howard J. Rosen, Maria Luisa Gorno‐Tempini

Bibliographic record

VenueCortex · 2016
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsnot available
FundersNational Institute of Neurological Disorders and StrokeNational Institute on Deafness and Other Communication DisordersNational Institute on AgingUniversity of California, San FranciscoJohn Douglas French Alzheimer's FoundationNational Institutes of HealthCanadian Centre for Applied Research in Cancer ControlState of CaliforniaLarry L. Hillblom FoundationU.S. Department of Veterans Affairs
KeywordsPrimary progressive aphasiaPsychologyDyslexiaSemantic dementiaAphasiaNeuroimagingLateralization of brain functionAffect (linguistics)Right hemisphereTemporal lobeAudiologyCognitive psychologyReading (process)Semantic memoryDementiaNeuroscienceCognitionMedicinePathologyLinguisticsDiseaseCommunicationFrontotemporal dementiaEpilepsy

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.309
Teacher spread0.284 · 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 designObservational
Domainnot available
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

Citations104
Published2016
Admission routes1
Has abstractno

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