Bibliographic record
Abstract
Every year hundreds of individuals contribute their time and expertise to help Basin Research maintain unbiased, professional, independent and expert-driven peer review of all manuscripts that are eventually published in our journal. The reviewers receive no remuneration, and we are indebted and extremely grateful to our reviewers for their service to this journal, and to the scientific community. Our reviewers are as diverse as they are excellent, representing countries and institutions across the globe, and spanning career stages from PhD students to Professors Emeriti/Emerita. We would like to celebrate some of the individuals who contributed with expert reviews for Basin Research in the past year, by publishing a list of outstanding reviewers that we want to acknowledge and recognize as such. These reviewers will also receive a certificate to give recognition for their significant contributions. Although it is not possible to list everyone, we would like to express our sincere thanks to all reviewers who contributed with reviews of manuscripts for Basin Research in the past year. We would also like to extend a special thanks to all early-career researchers who reviewed for Basin Research in the past year; we are particularly impressed with the quality and rigor you bring to the Basin Research peer-review process. List of outstanding reviewers 2018-2019: Gary Axen New Mexico Institute of Mining and Technology, Mexico Thomas Berg Kristensen Equinor ASA, Norway Anne Bernhardt Freie Universität Berlin, Germany Johan Claringbould Tokyo Daigaku Jishin Kenkyujo, Japan Grace Cosgrove University of Leeds, UK Sian Evans Imperial College London, UK Mary Ford University of Lorraine, Nancy, France Derya Gürer University of Queensland, Australia Elizabeth Hajek Penn State University, USA David Hodgson University of Leeds, UK Dale Issler Natural Resources Canada, Alberta, Canada Lara Kalnins University of Edinburgh, UK Carolyn Lampe ucon Geoconsulting, Koeln, Germany Francisco Lobo CSIC, Granada, Spain Yitzaq Makovsky University of Haifa, Israel Michael McGlue University of Kentucky, USA Ivar Midtkandal University of Oslo, Norway Lorena Moscardelli University of Texas, USA Thomas Phillips University of Durham, UK Leonardo Muniz Pichel Imperial College London, UK Clara Rodriguez Schlumberger, Western Geco, USA Lydia Staisch USGS, California, USA Zoltan Sylvester University of Texas, USA Torbjorn Tornqvist Tulane University, USA Fabio Trincardi Istituto di Scienze Marine Consiglio Nazionale delle Ricerche, Italy Sean Willett ETH Zürich, Switzerland Kirstie Wright Heriot Watt University, Edinburgh, UK
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 | 0.025 | 0.101 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.021 | 0.008 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.221 | 0.237 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".