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Record W1566125657 · doi:10.1002/0471142301.ns0210s36

Whole‐Mount Immunohistochemistry of the Brain

2006· article· en· W1566125657 on OpenAlexaff
Se Hoon Kim, Priscilla Che, Seung‐Hyuk Chung, Debora Doorn, Monica Hoy, Matt Larouche, Hassan Marzban, Justyna R. Sarna, Sepehr Zahedi, Richard Hawkes

Bibliographic record

VenueCurrent Protocols in Neuroscience · 2006
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGlycosylation and Glycoproteins Research
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsImmunocytochemistryComputer scienceMountImmunohistochemistryArtificial intelligenceComputer visionPathologyMedicine

Abstract

fetched live from OpenAlex

The gross anatomical distribution of an antigen is typically mapped using a combination of serial sectioning, immunocytochemistry, and three-dimensional reconstruction. This is a tedious and time-consuming procedure, which introduces an array of potential alignment and differential shrinkage errors and requires considerable experience and specialized equipment. In particular, it is unsuited for routine screening applications. To circumvent these problems, this unit presents a routine whole-mount immunocytochemistry protocol that can be used to map many antigenic distributions in the developing and adult brain. The technique can also be easily adapted to detect anterograde and retrograde transport tracers.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.319
Threshold uncertainty score0.293

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 science0.0010.000
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 head0.025
GPT teacher head0.360
Teacher spread0.335 · 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 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

Citations7
Published2006
Admission routes1
Has abstractyes

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