MétaCan
Menu
Back to cohort
Record W3153528569 · doi:10.1016/j.ekir.2021.03.087

POS-080 RNASCOPE MULTIPLEX IN SITU HYBRIDIZATION: A POWERFUL COMPLEMENT FOR STUDIES OF PROTEIN-PROTEIN INTERACTIONS IN COMPLEX TISSUES.

2021· article· en· W3153528569 on OpenAlexaff
Serge Lemay, A. Masztalerz, C. Baldwin, E. Chen, Tomoko Takano

Bibliographic record

VenueKidney International Reports · 2021
Typearticle
Languageen
FieldMedicine
TopicMonoclonal and Polyclonal Antibodies Research
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsMultiplexComputational biologyMedicineIn situIn situ hybridizationAntibodyBioinformaticsBiologyImmunologyBiochemistryChemistryMessenger RNA

Abstract

fetched live from OpenAlex

Protein-protein interactions form the basis most physiological and pathophysiological mechanisms, but studying them at the tissue level is challenging, especially when they are transient, weak or involve low-abundance molecules in complex tissues. The problem is compounded when using antibody-based approaches, due to the paucity of rigorously validated antibodies for in situ studies.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.022
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0220.007

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 head0.103
GPT teacher head0.428
Teacher spread0.325 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueKidney International ReportsSame topicMonoclonal and Polyclonal Antibodies ResearchFrench-language works237,207