MétaCan
Menu
Back to cohort
Record W2948949781 · doi:10.1016/j.neuron.2019.05.002

SynGO: An Evidence-Based, Expert-Curated Knowledge Base for the Synapse

2019· article· en· W2948949781 on OpenAlexaff
Frank Koopmans, Pim van Nierop, Maria Andres‐Alonso, Andrea Byrnes, Tony Cijsouw, Marcelo P. Coba, L. Niels Cornelisse, Ryan Farrell, Hana L. Goldschmidt, Daniel P. Howrigan, Natasha K. Hussain, Cordelia Imig, Arthur P.H. de Jong, Hwajin Jung, Mahdokht Kohansal-Nodehi, Barbara Kramarz, Noa Lipstein, Ruth C. Lovering, Harold D. MacGillavry, Vittoria Mariano, Huaiyu Mi, Momchil Ninov, David Osumi-Sutherland, Rainer Pielot, Karl‐Heinz Smalla, Haiming Tang, Katherine Tashman, Ruud F. Toonen, Chiara Verpelli, Rita Reig‐Viader, Kyoko Watanabe, Jan R.T. van Weering, Tilmann Achsel, Ghazaleh Ashrafi, Nimra Asi, Tyler C. Brown, Pietro De Camilli, Marc Feuermann, Rebecca E. Foulger, Pascale Gaudet, Anoushka Joglekar, Alexandros K. Kanellopoulos, Robert Malenka, Roger A. Nicoll, Camila Pulido, Jaime de Juan‐Sanz, Morgan Sheng, Thomas C. Südhof, Hagen Tilgner, Claudia Bagni, Àlex Bayés, Thomas Biederer, Nils Brose, John Jia En Chua, Daniela C. Dieterich, Eckart D. Gundelfinger, Casper C. Hoogenraad, Richard L. Huganir, Reinhard Jahn, Pascal S. Kaeser, Eunjoon Kim, Michael R. Kreutz, Peter S. McPherson, Ben Neale, Vincent O’Connor, Daniëlle Posthuma, Timothy A. Ryan, Carlo Sala, Guoping Feng, Steven E. Hyman, Paul D. Thomas, August B. Smit, Matthijs Verhage

Bibliographic record

VenueNeuron · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBioinformatics and Genomic Networks
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
FundersNational Institute on Drug AbuseNational Institute of Mental HealthLeibniz-GemeinschaftNational Institutes of HealthNational Institute of Neurological Disorders and StrokeGeneralitat de CatalunyaBundesministerium für Bildung und ForschungStanley Center for Psychiatric Research, Broad InstituteCentres de Recerca de CatalunyaDeutsche ForschungsgemeinschaftErzincan ÜniversitesiEuropean Regional Development FundBroad InstituteEuropean CommissionEU Joint Programme – Neurodegenerative Disease Research
KeywordsSynapseGeneNeuroscienceGene ontologyBiologyAutismAnnotationOntologyComputational biologySchizophrenia (object-oriented programming)Computer scienceGeneticsPsychologyPsychiatryGene expression

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.001
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0060.003
Science and technology studies0.0010.000
Scholarly communication0.0030.002
Open science0.0030.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0150.006

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 head0.030
GPT teacher head0.280
Teacher spread0.250 · 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 designNot applicable
Domainnot available
GenreDataset

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

Citations1,031
Published2019
Admission routes1
Has abstractno

Explore more

Same venueNeuronSame topicBioinformatics and Genomic NetworksFrench-language works237,207