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Record W2152860631 · doi:10.1016/j.bbr.2014.01.043

Neural correlates of nesting behavior in zebra finches (Taeniopygia guttata)

2014· article· en· W2152860631 on OpenAlexfundno aff
Zachary J. Hall, Marion Bertin, Ida E. Bailey, Simone Meddle, Susan D. Healy

Bibliographic record

VenueBehavioural Brain Research · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAnimal Vocal Communication and Behavior
Canadian institutionsnot available
FundersBiotechnology and Biological Sciences Research CouncilNatural Sciences and Engineering Research Council of Canada
KeywordsTaeniopygiaNest (protein structural motif)Zebra finchNesting (process)Context (archaeology)NeuroscienceBiologyBehavioral patternComputer science

Abstract

fetched live from OpenAlex

Nest building in birds involves a behavioral sequence (nest material collection and deposition in the nest) that offers a unique model for addressing how the brain sequences motor actions. In this study, we identified brain regions involved in nesting behavior in male and female zebra finches (Taeniopygia guttata). We used Fos immunohistochemistry to quantify production of the immediate early gene protein product Fos (a molecular indicator of neuronal activity) in the brain correlated this expression with the variation in nesting behavior. Using this technique, we found that neural circuitry involved in motor sequencing, social behavior, reward and motivation were active during nesting. Within pairs of nesting birds, the number of times a male picked up or deposited nesting material and the amount of time a female spent in the nest explained the variation in Fos expression in the anterior motor pathway, social behavior network, and reward neural circuits. Identification of the brain regions that are involved in nesting enables us to begin studying the roles of motor sequencing, context, and reward in construction behavior at the neural level.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.370
Threshold uncertainty score0.614

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.125
GPT teacher head0.398
Teacher spread0.273 · 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 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

Citations43
Published2014
Admission routes1
Has abstractyes

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