MétaCan
Menu
Back to cohort

Antibody Development and Use in Chromogenic and Fluorescent Immunostaining

2012· article· en· W52674165 on OpenAlexaff
Eamon Dubaissi, Niki Panagiotaki, Nancy Papalopulu, Peter D. Vize

Bibliographic record

VenueMethods in molecular biology · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCongenital heart defects research
Canadian institutionsUniversity of Calgary
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institutes of HealthWellcome Trust
KeywordsImmunostainingXenopusImmunofluorescenceAntibodyBiologyEmbryoMolecular biologyImmunohistochemistryCell biologyImmunologyGeneGenetics

Abstract

fetched live from OpenAlex

Antibody-based detection of protein distribution patterns both in wholemount and on sections revolutionized Xenopus research and ushered in the visual-based era of Xenopus data presentation. The ability to view the distribution of a gene product throughout an embryo makes it possible to rapidly map normal expression profiles and profiles that have been altered by an experimental intervention. The main limiting element in Xenopus immunostaining techniques has always been the availability of antibodies that work well on fixed whole embryos, a problem that persists. However, new antibodies are constantly being generated and improvements in detection systems allow antibodies that were once below the limits of detection to be utilized in multichannel immunofluorescence using tyramide amplification.

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)0.005
metaresearch head score (Gemma)0.005
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: Methods · Consensus signal: Methods
Teacher disagreement score0.013
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.003
Science and technology studies0.0020.002
Scholarly communication0.0020.002
Open science0.0030.002
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0130.021

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 head0.032
GPT teacher head0.422
Teacher spread0.390 · 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
GenreMethods

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

Citations8
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueMethods in molecular biologySame topicCongenital heart defects researchFrench-language works237,207