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Morphology of Invertebrate Neurons and Synapses

2017· book-chapter· en· W2626422039 on OpenAlexaff
Ian A. Meinertzhagen

Bibliographic record

VenueOxford University Press eBooks · 2017
Typebook-chapter
Languageen
FieldNeuroscience
TopicNeurobiology and Insect Physiology Research
Canadian institutionsDalhousie University
Fundersnot available
KeywordsNeuroscienceSynapseBiologyNeurotransmitterNeuronNervous systemNeurotransmitter receptorNeurotransmissionCentral nervous systemReceptor

Abstract

fetched live from OpenAlex

Abstract Despite their often small numbers, the neurons in invertebrate nervous systems can nevertheless constitute many classes, and the nervous systems of little studied or entirely new species still offer significant opportunities for discovery. Circuit analyses and connectomic data are of particular significance, as are the relationships of these to behavior, and the organization of simple larval brains. Functional analyses of synaptic circuits still require knowledge of the neurotransmitter and neurotransmitter receptor for each identified neuron. Synapse complexity ranges widely; undifferentiated pathways in basal species may have unpolarized synapses with presynaptic sites opposite each other, and specialized pathways may have polyadic synapses.

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: none
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.003

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 head0.054
GPT teacher head0.251
Teacher spread0.197 · 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

Citations13
Published2017
Admission routes1
Has abstractyes

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Same venueOxford University Press eBooksSame topicNeurobiology and Insect Physiology ResearchFrench-language works237,207