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Record W2168728070 · doi:10.1017/s0317167100009756

Damage of White Matter in the Parietal Lobe Results in Anomic Alexia of Kana

2010· article· en· W2168728070 on OpenAlexvenueno aff
Nobusada Shinoura, Yuichi Suzuki, Masanobu Tsukada, Ryozi Yamada, Yusuke Tabei, Tomoyuki Koizumi, Kazuo Yagi

Bibliographic record

VenueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2010
Typearticle
Languageen
FieldMedicine
TopicAdvanced Neuroimaging Techniques and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsKanaParietal lobePsychologyWhite matterAction (physics)White (mutation)NeuroscienceMedicineComputer sciencePhysicsBiologyArtificial intelligenceKanji

Abstract

fetched live from OpenAlex

The reading and writing of two different types of Japanese words, "kana" and "kanji", are processed by different neural pathways.For example, kana are phonographic representations of words that are processed though the dorsal route related to phonological processing in a manner similar to that for words of phonetic languages (e.g., English).Investigators have reported that the left angular gyrus is critical for the process of writing kana 1,2 .By contrast, kanji are logographic representations of words and are processed through the ventral route related to visual processing in a manner similar to that for logographic languages (e.g., Chinese) [3][4][5] .Investigators have reported that the left posterior inferior or middle temporal cortex is critical for the process of reading or writing kanji 2,6-8 .

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)0.000
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: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.053
GPT teacher head0.321
Teacher spread0.268 · 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 designCase report
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

Citations0
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques→Same topicAdvanced Neuroimaging Techniques and Applications→French-language works237,207→