Bibliographic record
Abstract
The editors and Karger Publishers would like to thank the following reviewers for their ongoing support in reviewing manuscripts for Cytogenetic and Genome Research:Robert B. Angus, Egham, UKKristina Aubell, Graz, AustriaIrina Y. Bakloushinskaya, Moscow, RussiaMir R. Bekheirnia, Houston, TX, USAMatthew T. Biegler, New York, NY, USALarisa Biltueva, Novosibirsk, RussiaVilhelm Bohr, Copenhagen, DenmarkDanon C. Cardoso, Ouro Preto, BrazilAcácia F.L. Carvalho, Salvador, BrazilNicolas Chatron, Bron, FranceWilliam Conway, Cambridge, MA, USASumito Dateki, Nagasaki, JapanAsya Davidian, Baltimore, MD, USAKelly Dawe, Athens, GA, USAEdivaldo H. C. de Oliveira, Ananindeua, BrazilDmitrij Dedukh, Libechov, CzechiaTiago Degrandi, Curitiba, BrazilHolli Drendel, Charlotte, NC, USAElena Drosopoulou, Thessaloníki, GreeceMunis Dundar, Kayseri, TurkeyChitho Feliciano, Quezon City, PhilippinesSvetlana A. Galkina, St. Petersburg, RussiaSujay Ghosh, Kolkata, IndiaPatrick R. Gonzales, Lenexa, KN, USAChrystian A.G. Haerter, Manaus, BrazilBen Hilton, Secaucus, NJ, USARon Hochstenbach, Amsterdam, The NetherlandsIvan Iourov, Moscow, RussiaM. Anwar Iqbal, Rochester, NY, USASylvie Jaillard, Rennes, FrancePaul Kalitsis, Melbourne, VIC, AustraliaRie Kawamura, Toyoake, JapanMartin Knytl, Prague, CzechiaDavid Kopecký, Olomouc, CzechiaDieter Kotzot, Salzburg, AustriaRafael Kretschmer, Porto Alegre, BrazilRakesh Kumar, Katra, IndiaKenji Kurosawa, Tokyo, JapanIgor N. Lebedev, Tomsk, RussiaThomas Liehr, Jena, GermanySilvia Llambí, Montevideo, UruguayDenilce M. Lopes, Viçosa, BrazilLuciana B. Lourenço, São Paulo, BrazilHuiming Lu, Dallas, TX, USAAndrea Luchetti, Bologna, ItalyVladimir Lukhtanov, St. Petersburg, RussiaRhadia Mkacher, Paris, FranceKeith A. Maggert, Tucson, AZ, USAZuzana Majtánová, Libechov, CzechiaValérie Malan, Paris, FranceLyubov P. Malinovskaya, Novosibirsk, RussiaKeiko Matsubara, Tokyo, JapanJean McGowan-Jordan, Ottawa, ON, CanadaIkuo Miura, Higashi-Hiroshima, JapanAmal M. Mohamed, Cairo, EgyptDaniela Moralli, Oxford, UKGopeshwar Narayan, Varanasi, IndiaMasafumi Nozawa, Tokyo, JapanPatricia P. Parise-Maltempi, Rio Claro, BrazilEric L. Patterson, East Lansing, MI, USASvetlana V. Pavlova, Moscow, RussiaDijana Perovic, Belgrade, SerbiaVenkatachalam Perumal, Porur, IndiaMaría I. Pigozzi, Buenos Aires, ArgentinaHelia Pimentel, Würzburg, GermanyMartin Poot, Würzburg, GermanyLuca Proietti De Santis, Viterbo, ItalyMarija Rajičić, Belgrade, SerbiaTerje Raudsepp, College Station, TX, USAJosephine Reinhardt, Geneseo, NY, USATamas Revay, Calgary, AB, CanadaLucia Rocco, Caserta, ItalyTerri L. Ryan, Oak Ridge, TN, USAManisha Sachan, Prayagraj, IndiaAlsu F. Saifitdinova, St. Petersburg, RussiaFrancisco M.C. Sassi, Chongqing, ChinaIngo Schubert, Gatersleben, GermanyVorasuk Shotelersuk, Bangkok, ThailandJulia Sidorova, Washington, DC, USASergey A. Simanovsky, Moscow, RussiaThomas Smol, Lille, FranceKornsorn Srikulnath, Bangkok, ThailandGanesh Subramaniam, Kanpur, IndiaYue-Qiu Tan, Changsha, ChinaRungroj Thangpong, Pathum Wan, ThailandKaterina V. Tishakova, Novosibirsk, RussiaYehuda Tzfati, Jerusalem, IsraelYoshinobu Uno, Tokushima, JapanRicardo Utsunomia, Rio de Janeiro, BrazilStanislav Vasilyev, Tomsk, RussiaGopalrao V.N. Velagaleti, San Antonio, TX, USAAlex Matheus Viana, Manaus, BrazilLingxi Wang, Chengdu, ChinaAnja Weise, Jena, GermanyYuliang Wu, Saskatoon, SK, CanadaKumiko Yanagi, Tokyo, JapanAtsuo Yoshido, Ceske Budejovice, CzechiaDiana Zheglo, Moscow, Russia
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.173 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.315 | 0.219 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".