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Cerebellum: Anatomy and Organisation

2019· other· en· W4256120867 on OpenAlexaff
Richard Hawkes

Bibliographic record

VenueEncyclopedia of Life Sciences · 2019
Typeother
Languageen
FieldNeuroscience
TopicVestibular and auditory disorders
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCerebellumNeuroscienceCerebellar cortexEfferentAfferentBiologyPurkinje cellDeep cerebellar nucleiAnatomyClimbing fiberGranule cellSensory systemCentral nervous system

Abstract

fetched live from OpenAlex

Abstract The cerebellum is a highly stereotyped cortical structure in the hindbrain of all vertebrates from fish to primates. The circuitry of the cerebellar cortex is built around the large, inhibitory Purkinje cells, which are the focus of all afferent input to the cerebellar cortex and are modulated by several classes of inhibitory interneuron (principally basket, stellate, and Golgi cells). Despite the apparent homogeneity of the cerebellar circuitry, the cerebellum is highly modular, comprising several hundred discrete and reproducible anatomical and physiological units (‘stripes’). Each stripe receives precise afferent inputs – climbing fibres directly to the Purkinje cells and mossy fibres indirectly via the granule cells. In turn, the Purkinje cells send efferent projections to specific targets in the cerebellar and vestibular nuclei. As a result, within the cerebellum a wide variety of sensory information is brought together and integrated, primarily to aid in motor control but also serving multiple other functions. Key Concepts Cerebellar circuitry is built around the Purkinje cell. The cerebellar cortex is divided into an array of transverse zones and parasagittal stripes. The cerebellar cortex receives two major afferent inputs – climbing fibres and mossy fibres. Purkinje cells are the sole efferent projections of the cerebellar cortex. Multiple interneurons modulate Purkinje cell firing patterns.

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)0.000
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: Other · Consensus signal: Other
Teacher disagreement score0.011
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.002
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.004

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 head0.015
GPT teacher head0.260
Teacher spread0.245 · 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
GenreOther

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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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