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Record W2985178636 · doi:10.1111/tra.12710

The evolution of the axonal transport toolkit

2019· review· en· W2985178636 on OpenAlexfundno aff
Sunaina Surana, David Villarroel‐Campos, Oscar M. Lazo, Edoardo Moretto, Andrew P. Tosolini, Elena R. Rhymes, Sandy Richter, James N. Sleigh, Giampietro Schiavo

Bibliographic record

VenueTraffic · 2019
Typereview
Languageen
FieldNeuroscience
TopicNeurogenesis and neuroplasticity mechanisms
Canadian institutionsnot available
FundersComisión Nacional de Investigación Científica y TecnológicaMedical Research CouncilHuman Frontier Science ProgramMotor Neurone Disease AssociationHorizon 2020 Framework ProgrammeMedical Research Council CanadaWellcome Trust
KeywordsNeuroscienceBiologyAxoplasmic transportAxonProcess (computing)Cognitive scienceComputer sciencePsychology

Abstract

fetched live from OpenAlex

Neurons are highly polarized cells that critically depend on long-range, bidirectional transport between the cell body and synapse for their function. This continual and highly coordinated trafficking process, which takes place via the axon, has fascinated researchers since the early 20th century. Ramon y Cajal first proposed the existence of axonal trafficking of biological material after observing that dissociation of the axon from the cell body led to neuronal degeneration. Since these first indirect observations, the field has come a long way in its understanding of this fundamental process. However, these advances in our knowledge have been aided by breakthroughs in other scientific disciplines, as well as the parallel development of novel tools, techniques and model systems. In this review, we summarize the evolution of tools used to study axonal transport and discuss how their deployment has refined our understanding of this process. We also highlight innovative tools currently being developed and how their addition to the available axonal transport toolkit might help to address key outstanding questions.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.996
Threshold uncertainty score0.582

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.082
GPT teacher head0.293
Teacher spread0.211 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations35
Published2019
Admission routes1
Has abstractyes

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