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Record W4220678973 · doi:10.2139/ssrn.4065080

Defining Microglial States and Nomenclature: A Roadmap to 2030

2022· article· en· W4220678973 on OpenAlexaff
Rosa Chiara Paolicelli, Amanda Sierra, Beth Stevens, Marie‐Ève Tremblay, Adriano Aguzzi, Bahareh Ajami, Ido Amit, Étienne Audinat, Ingo Bechmann, Mariko L. Bennett, F. Chris Bennett, Alain Bessis, Knut Biber, Staci D. Bilbo, Mathew Blurton‐Jones, Erik Boddeke, Dora Brites, Bert Brône, Guy C. Brown, Oleg Butovsky, Monica J. Carson, Bernardo Castellano, Marco Colonna, Sally A. Cowley, Colm Cunningham, Dimitrios Davalos, Philip L. De Jager, Bart De Strooper, Ádám Dénes, Bart J. L. Eggen, Ukpong B. Eyo, Elena Galea, Sonia Garel, Florent Ginhoux, Christopher K. Glass, Özgün Gökçe, Diego Gómez‐Nicola, Berta González, Siamon Gordon, Manuel B. Graeber, Andrew D. Greenhalgh, Pierre Gressèns, Melanie Greter, David H. Gutmann, Christian Haass, Michael T. Heneka, Frank L. Heppner, Soyon Hong, Steffen Jung, Helmut Kettenmann, Jonathan Kipnis, Ryuta Koyama, Greg Lemke, Marina A. Lynch, Ania K. Majewska, Marzia Malcangio, Tarja Malm, Renzo Mancuso, Michela Matteoli, Barry W. McColl, Véronique E. Miron, Anna V. Molofsky, Michelle Monje, Éva Mracskó, Agnès Nadjar, Jonas J. Neher, Urtė Neniškytė, Harald Neumann, Mami Noda, Bo Peng, Francesca Peri, V. Hugh Perry, Phillip G. Popovich, Josef Priller, Davide Ragozzino, Richard M. Ransohoff, Michael W. Salter, Anne Schaefer, Dorothy P. Schafer, Michal Schwartz, Mikael Simons, Wolfgang R. Streit, Tuan Leng Tay, Li‐Huei Tsai, Alexei Verkhratsky, Rommy von Bernhardi, Hiroaki Wake, Valérie Wittamer, Susanne A. Wolf, Long‐Jun Wu, Tony Wyss‐Coray

Bibliographic record

VenueSSRN Electronic Journal · 2022
Typearticle
Languageen
FieldNeuroscience
TopicNeuroinflammation and Neurodegeneration Mechanisms
Canadian institutionsUniversity of VictoriaHospital for Sick ChildrenUniversité Laval
Fundersnot available
KeywordsDichotomyMicrogliaMultidisciplinary approachCognitive sciencePerspective (graphical)NeuroscienceComputer scienceData scienceBiologyEngineering ethicsPolitical scienceSociologyEpistemologyPsychologyArtificial intelligenceImmunologyInflammationEngineeringSocial science

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)0.037
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.037
Threshold uncertainty score0.194

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0370.026
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0070.005
Science and technology studies0.0020.008
Scholarly communication0.0100.018
Open science0.0060.008
Research integrity0.0070.008
Insufficient payload (model declined to judge)0.0130.011

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 head0.009
GPT teacher head0.234
Teacher spread0.225 · 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 designTheoretical or conceptual
Domainnot available
GenreReview

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

Citations33
Published2022
Admission routes1
Has abstractno

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