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Record W1980444386 · doi:10.1155/2015/975367

Odorant Receptors Signaling Instructs the Development and Plasticity of the Glomerular Map

2015· review· en· W1980444386 on OpenAlexaff
Pablo Valle‐Leija

Bibliographic record

VenueNeural Plasticity · 2015
Typereview
Languageen
FieldNeuroscience
TopicOlfactory and Sensory Function Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsNeuroscienceOlfactory systemOlfactory bulbOlfactory epitheliumSensory systemGlomerulusBiologyContext (archaeology)OlfactionBiological neural networkCentral nervous systemNervous systemEndocrinologyKidney

Abstract

fetched live from OpenAlex

The olfactory system provides a great opportunity to explore the mechanisms that underlie the formation and function of neural circuits because of the simplicity of its structure. Olfactory sensory neurons (OSNs) located in the peripheral olfactory epithelium (OE) take part in the initial formation and function of glomeruli in the olfactory bulb (OB) inside the central nervous system. Glomeruli are key in the process of transduction of olfactory information, as they constitute a map in the OB that sorts the different types of odorant inputs. This odorant categorization allows proper olfactory perception, and it is achieved through the anatomical organization and function of the different glomerular circuits. Once formed, glomeruli keep the capacity to undergo diverse plasticity processes, which is unique among the different neural circuits of the central nervous system. In this context, through the expression and function of the odorant receptors (ORs), OSNs perform two of the most important roles in the olfactory system: transducing odorant information to the nervous system and initiating the development of the glomerular map to organize olfactory information. This review addresses essential information that has emerged in recent years about the molecular basis of these processes.

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)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.260
GPT teacher head0.307
Teacher spread0.048 · 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

Citations14
Published2015
Admission routes1
Has abstractyes

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