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Record W2074941812 · doi:10.1002/jemt.10138

Neuromodulation of arthropod mechanosensory neurons

2002· review· en· W2074941812 on OpenAlexafffund
Päivi H. Torkkeli, Izabela Panek

Bibliographic record

VenueMicroscopy Research and Technique · 2002
Typereview
Languageen
FieldNeuroscience
TopicNeurobiology and Insect Physiology Research
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsNeuromodulationNeuroscienceArthropodMechanoreceptorBiologyStimulationEcology

Abstract

fetched live from OpenAlex

Arthropod mechanosensory afferents have long been known to receive efferent synaptic connections onto their centrally located axon terminals. These connections cause presynaptic inhibition by attenuating the action potentials arriving at the axon terminals, thus reducing the synaptic potentials in the postsynaptic neurons. This type of inhibition can specifically reduce the excitation of selected postsynaptic neurons while leaving others unaffected. However, recent research has demonstrated that sensory signals detected by arthropod mechanosensory neurons can also be synaptically modulated before they ever arrive at the axon terminals. In arachnids and crustaceans, wide and complex networks of synapses on all parts of the afferent neurons, including the somata and dendrites, provide mechanisms to inhibit or enhance the responses to mechanical stimuli as they are being detected. This modulation will affect the signal transmission to all axonal branches and postsynaptic cells of the affected receptor neuron. In addition to the increased complexity of mechanosensory information transmission produced by these synapses, a variety of circulating neuroactive substances also modulate these neurons by acting on their postsynaptic receptors.

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: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.003

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 head0.213
GPT teacher head0.453
Teacher spread0.240 · 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
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

Citations23
Published2002
Admission routes2
Has abstractyes

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