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Record W4389455053 · doi:10.1016/j.mex.2023.102516

Rodent brain extraction and dissection: A comprehensive approach

2023· article· en· W4389455053 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMethodsX · 2023
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury and Neurovascular Disturbances
Canadian institutionsDalhousie University
FundersAl-Balqa' Applied University
KeywordsNeuroscienceStriatumHippocampusPrefrontal cortexThalamusCortex (anatomy)Cerebral cortexComputer sciencePsychology

Abstract

fetched live from OpenAlex

The neuroscience is continuously expanding field, and conducting experiments serves as one of the most effective approaches to enhance and broad our understanding of this fascinating field. Most of the lab work in neuroscience involves the use of animal models such as rats and mice for experiments dedicated to monitoring cerebral changes. The study: • Introduces a practical method for brain extraction without perfusion with paraformaldehyde prioritizing brain integrity and avoiding damage. • Offers a detailed, step-by-step dissection guide for different brain regions, including the hippocampus, cerebral cortex, corpus striatum, thalamus, cerebellum, and medial prefrontal cortex, from rodent brains, accompanied by high-resolution images that provide anatomical clarity. • Presents enhanced reliability, precision, and detailed anatomical descriptions. Conclusion : This study has introduced a reliable technique for brain extraction that eliminates the need for paraformaldehyde perfusion. Furthermore, a comprehensive methodology has been presented for extracting different brain regions from rodent brains.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.886
Threshold uncertainty score0.326

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.089
GPT teacher head0.384
Teacher spread0.295 · 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