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Record W2091600211 · doi:10.1002/hipo.20136

Medial septal modulation of the ascending brainstem hippocampal synchronizing pathways in the freely moving rat

2005· article· en· W2091600211 on OpenAlexaff
Brian H. Bland, J. Andrew Bird, Jesse Jackson, Kiyohisa Natsume

Bibliographic record

VenueHippocampus · 2005
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neuropharmacology Research
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsStimulationHippocampal formationNeuroscienceHippocampusChemistryBrainstemIntensity (physics)PsychologyPhysics

Abstract

fetched live from OpenAlex

Rats implanted with hippocampal recording electrodes were tested in a wheel-running apparatus under three conditions: (1) independent electrical stimulation of the medial septal nucleus (MS); (2) independent electrical stimulation of the posterior hypothalamic nucleus (PH); and (3) combined electrical stimulation of the MS and PH using pairings of two stimulation conditions, 7 or 10 Hz stimulation of the MS, and a low- or high-intensity PH stimulation. Quantitative measures of running speed were taken, and hippocampal recordings were subjected to fast-Fourier transform analysis. Electrical stimulation of the PH induced wheel-running behavior; running speed and the accompanying hippocampus (HPC) theta frequency increased with increase in stimulation intensity. Electrical stimulation of the MS failed to induce wheel-running behavior despite the fact that HPC theta was induced at the frequency of the applied stimulation (7 and 10 Hz). Electrical stimulation of the MS reset the frequency of HPC theta induced by PH stimulation in both the upward and downward directions and increased theta power, while wheel-running speed was modulated in a downward direction only.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.053
GPT teacher head0.302
Teacher spread0.249 · 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 designBench or experimental
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

Citations23
Published2005
Admission routes1
Has abstractyes

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