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Involvement of the Sex Hormones in Learning and Memory

2019· reference-entry· en· W2970208187 on OpenAlexaff
Kelsy S.J. Ervin, Elena Choleris

Bibliographic record

Venuenot available
Typereference-entry
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicReceptor Mechanisms and Signaling
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsCognitionMemory consolidationHormonePsychologyNeuroscienceCognitive scienceDevelopmental psychologyBiologyCognitive psychologyHippocampus

Abstract

fetched live from OpenAlex

Learning and memory can be defined as the processes by which we acquire information about our environment and experiences, to be used later in similar situations. These cognitive processes are important for behaviors crucial to survival, such as finding food and shelter, assessing risk, and behaving appropriately in social contexts. Research provides insights into how hormones influence animal and human cognition. Here, we focus on the sex hormones, which influence both learning or acquisition and memory consolidation through two mechanisms: (i) the genomic pathways, acting longer-term to modulate gene transcription, protein expression, and structural changes implicated in establishing memory traces; and (ii) rapid pathways in which receptor activation initiates cell signaling cascades and more immediate neural responses. Investigating the roles of sex hormones in learning and memory enhances our understanding of neural networks involved in different types of learning, in ways that may be applied to human health and cognition.

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: Other · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.053

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)0.0160.004

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 head0.013
GPT teacher head0.235
Teacher spread0.223 · 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
GenreOther

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

Citations1
Published2019
Admission routes1
Has abstractyes

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