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Record W3141685688 · doi:10.1038/s41586-021-03473-8

Author Correction: Dense sampling of bird diversity increases power of comparative genomics

2021· article· en· W3141685688 on OpenAlexaff
Shaohong Feng, Josefin Stiller, Yuan Deng, Joel Armstrong, Qi Fang, Andrew Hart Reeve, Duo Xie, Guangji Chen, Chunxue Guo, Brant C. Faircloth, Bent Petersen, Zongji Wang, Qi Zhou, Mark Diekhans, Wanjun Chen, Sergio Andreu‐Sánchez, Ashot Margaryan, Jason T. Howard, Carole A. Parent, George Pacheco, Mikkel‐Holger S. Sinding, Lara Puetz, Emily Louisa Cavill, Ângela M. Ribeiro, Leopold Eckhart, Jon Fjeldså, Peter A. Hosner, Robb T. Brumfield, Les Christidis, Mads F. Bertelsen, Thomas Sicheritz‐Pontén, Dieter Thomas Tietze, Bruce C. Robertson, Gang Song, Gerald Borgia, Santiago Claramunt, Irby J. Lovette, Saul Cowen, Peter Njoroge, John P. Dumbacher, Oliver A. Ryder, Jérôme Fuchs, Michael Bunce, David W. Burt, Joël Cracraft, Guanliang Meng, Shannon J. Hackett, Peter G. Ryan, Knud A. Jønsson, Ian G. Jamieson, Rute R. da Fonseca, Edward L. Braun, Peter Houde, Siavash Mirarab, Alexander Suh, Bengt Hansson, Suvi Ponnikas, Hanna Sigeman, Martin Stervander, Paul B. Frandsen, Henriëtte van der Zwan, Rencia van der Sluis, Carina Visser, Christopher N. Balakrishnan, Andrew G. Clark, John W. Fitzpatrick, Reed Bowman, Nancy Chen, Alison Cloutier, Timothy B. Sackton, Scott V. Edwards, Dustin J. Foote, Subir B. Shakya, Frederick H. Sheldon, Alain Vignal, André E. R. Soares, Beth Shapiro, Jacob González‐Solís, Joan Ferrer, Julio Rozas, Marta Riutort, Anna Tigano, Vicki L. Friesen, Love Dalén, Araxi O. Urrutia, Tamás Székely, Yang Liu, Michael G. Campana, André Corvelo, Robert C. Fleischer, Kim Rutherford, Neil J. Gemmell, Nicolás Dussex, Henrik Mouritsen, Nadine Thiele, Kira E. Delmore, Miriam Liedvogel, André Franke, Marc P. Hoeppner, Oliver Krone, Adam M. Fudickar, Borja Milá, Ellen D. Ketterson, Andrew E. Fidler, Guillermo Friis, Ángela M. Parody-Merino, Phil F. Battley, Murray P. Cox, Nicholas Costa Barroso Lima, Francisco Prosdocimi, Thomas L. Parchman, Barney A. Schlinger, Bette A. Loiselle, John G. Blake, Haw Chuan Lim, Lainy B. Day, Matthew J. Fuxjager, Maude W. Baldwin, Michael J. Braun, Morgan Wirthlin, Rebecca B. Dikow, Thomas B. Ryder, Glauco Camenisch, Lukas F. Keller, Jeffrey M. DaCosta, Márk E. Hauber, Matthew I. M. Louder, Christopher C. Witt, Jimmy A. McGuire, Joann Mudge, Libby C. Megna, Matthew D. Carling, Biao Wang, Scott A. Taylor, Glaucia Del‐Rio, Alexandre Luis Padovan Aleixo, Ana Tereza Ribeiro de Vasconcelos, Claudio V. Mello, Jason T. Weir, David Haussler, Qiye Li, Huanming Yang, Jian Wang, Fumin Lei, Carsten Rahbek, M. Thomas P. Gilbert, Gary R. Graves, Erich D. Jarvis, Benedict Paten, Guojie Zhang

Bibliographic record

VenueNature · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsQueen's UniversityThe Scarborough HospitalRoyal Ontario MuseumUniversity of Toronto
FundersNational Human Genome Research InstituteNational Heart, Lung, and Blood InstituteVillum Fonden
KeywordsDiversity (politics)Sampling (signal processing)Power (physics)GenomicsComputational biologyEvolutionary biologyBiologyComputer sciencePhysicsGeneticsTelecommunicationsGenomeSociologyAnthropologyGene

Abstract

fetched live from OpenAlex

A Correction to this paper has been published: https://doi.org/10.1038/s41586-021-03473-8.

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)0.096
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: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.080
Threshold uncertainty score0.268

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.096
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.003
Science and technology studies0.0030.003
Scholarly communication0.0040.003
Open science0.0030.002
Research integrity0.0050.011
Insufficient payload (model declined to judge)0.0800.037

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 head0.044
GPT teacher head0.291
Teacher spread0.247 · 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
GenreOther

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

Citations15
Published2021
Admission routes1
Has abstractyes

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