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Record W4366162939 · doi:10.1038/s41592-023-01848-5

BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

2023· article· en· W4366162939 on OpenAlexaff
Linus Manubens-Gil, Zhi Zhou, Hanbo Chen, Arvind Ramanathan, Xiaoxiao Liu, Yufeng Liu, Alessandro Bria, Todd A. Gillette, Zongcai Ruan, Jian Yang, Miroslav Radojević, Ting Zhao, Li Cheng, Lei Qu, Siqi Liu, Kristofer E. Bouchard, Lin Gu, Weidong Cai, Shuiwang Ji, Badrinath Roysam, Ching‐Wei Wang, Hongchuan Yu, Amos Sironi, Daniel Maxim Iascone, Jie Zhou, Erhan Bas, Eduardo Conde‐Sousa, Paulo Aguiar, Xiang Li, Yujie Li, Sumit Nanda, Yuan Wang, Leila Mureşan, Pascal Fua, Bing Ye, Hai‐yan He, Jochen F. Staiger, Manuel Peter, Daniel N. Cox, Michel Simonneau, Marcel Oberlaender, Gregory S.X.E. Jefferis, Kei Ito, Paloma T. Gonzalez-Bellido, Jinhyun Kim, Edwin W. Rubel, Hollis T. Cline, Hongkui Zeng, Aljoscha Nern, Ann‐Shyn Chiang, Jianhua Yao, Jane Roskams, Rick Livesey, Janine Stevens, Tianming Liu, Chinh Dang, Yike Guo, Ning Zhong, Georgia D. Tourassi, Sean Hill, Michael Hawrylycz, Christof Koch, Erik Meijering, Giorgio A. Ascoli

Bibliographic record

VenueNature Methods · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCell Image Analysis Techniques
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental HealthUniversity of British ColumbiaUniversity of Alberta
FundersOak Ridge National LaboratoryNational Institute of Biomedical Imaging and BioengineeringNational Institute of Neurological Disorders and StrokeNational Eye InstituteLawrence Berkeley National LaboratoryNational Institute of Mental HealthImperial College LondonSoutheast UniversityEngineering and Physical Sciences Research CouncilÉcole Polytechnique Fédérale de LausanneU.S. Department of EnergyTencentNational Institutes of HealthUniversity of CambridgeAgence Nationale de la RechercheWellcome TrustBeijing University of TechnologyHoward Hughes Medical Institute
KeywordsComputer scienceTracingBenchmarkingBenchmark (surveying)Data miningVisualizationSet (abstract data type)Cluster analysisArtificial intelligenceData setRandom forestGround truthMachine learningPattern recognition (psychology)

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)0.006
metaresearch head score (Gemma)0.020
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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.020
Meta-epidemiology (narrow)0.0040.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0040.004
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0060.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0090.007

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 head0.010
GPT teacher head0.378
Teacher spread0.367 · 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

Citations55
Published2023
Admission routes1
Has abstractno

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