MétaCan
Menu
Back to cohort

Inverse Relationship between Release Probability and Readily Releasable Vesicles in Depressing and Facilitating Synapses

2002· article· en· W1912585044 on OpenAlexafffund
Andrew G. Millar, Haymo Bradacs, Milton P. Charlton, Harold L. Atwood

Bibliographic record

VenueJournal of Neuroscience · 2002
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCellular transport and secretion
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health ResearchUniversity of Toronto
KeywordsExocytosisVesicleSynaptic vesicleExcitatory postsynaptic potentialBiophysicsTonic (physiology)GlutamatergicNeuroscienceActive zoneBiologySynapseChemistryMembraneBiochemistryGlutamate receptorInhibitory postsynaptic potential

Abstract

fetched live from OpenAlex

We tested the hypothesis that the probability of vesicular exocytosis at synapses is positively correlated with the pools of readily releasable synaptic vesicles, as shown for mammalian neurons grown in tissue culture. We compared synapses of two identified glutamatergic neurons: phasic (high-output, depressing) and tonic (low-output, facilitating) crustacean motor neurons, which differ 100- to 1000-fold in quantal content. Estimates of vesicles available for exocytosis were made from depletion during forced release and from electron microscopic observation of vesicles docked at synaptic membranes near active zones. Both measurements showed a significantly larger pool of readily releasable vesicles in facilitating synapses, despite their much lower quantal output during stimulation. Thus, the probability for release of docked vesicles is very much lower at facilitating synapses, and the presence of more docked vesicles does not predict higher synaptic release probability in these paired excitatory neurons.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.053
GPT teacher head0.255
Teacher spread0.202 · 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

Citations72
Published2002
Admission routes2
Has abstractyes

Explore more

Same venueJournal of NeuroscienceSame topicCellular transport and secretionFrench-language works237,207