Thank You to Our 2017 Peer Reviewers
Bibliographic record
Abstract
We wish to thank all the reviewers who have made the publication of Earth and Space Sciences possible. We are a relatively new journal just closing up on 5 years of publishing papers on nontraditional topics including new methods, sensors, instruments, and modeling. Because this has been a major departure within AGU to support scientists and engineers involved in the complexities of data collection and analysis, we have relied upon all of you to meet that challenge. We anticipate the publication of the citation indices for the new journal in the near future and trust that it will demonstrate the value of this publication to the scientific community. Again, thanks for your willingness to address this new direction in publishing and hope you will all consider publishing in the journal itself. Individuals in italics provided three or more reviews for Earth and Space Science during the year. Alden, Caroline Ames, Daniel Ao, Chi Appleby, Graham Arnold, Gabriele Bailey, Steve Beegle, Luther Bela, Megan Ben-Ari, Tamara Bernhardt, Paul Bowring, Simon Bytheway, Janice Caress, David Caylor, Kelly Chatterjee, Snigdhansu Chau, Jorge Chen, Yaning Chen, Yingying Choi, Yong-Sang Chulliat, Arnaud Ciesielski, Paul Connolly, Ronan Courtland, Leah Culverwell, Ian Davis, Anthony De Beurs, Kirsten De Lucas, Aline De Viron, Olivier Demir, Ibrahim DeSouza-Machado, Sergio Dessler, Andrew Dewey, Richard K. Duda, Timothy Dumbovic, Mateja Eckman, Richard Essawy, Bakinam Feldman, Daniel Feng, Nan Finlay, Christopher Finn, Carol Fletcher, Jennifer Friess, Udo Gatebe, Charles Glaze, Lori Goodall, Jonathan Gray, Steve Greenberger, Rebecca Gronoff, Guillaume Gu, Yu Haag, Scott Haley, Charlotte Hargitai, Henrik Harten, Gerard Heavens, Nicholas Helman, David Henderson, David Hooshyar, Milad Jain, Shaleen Jauer, Paulo Jimenez-Munoz, Juan-Carlos Johnson, Natasha Jucks, Ken Jukic, Damir Kass, David Keller, G. Kenda, Balthasar Kennedy, Jeffrey Kiefer, Walter Kim, WonMoo Kong, Dongxian Kripalan, Dr. Ramesh Kroll, Kayla Kumar, Anikender Li, King-Fai Liu, Cheng Lucas, Antoine Luo, Zhengzhao Lyzenga, Greg Ma, Jian Margolis, Jack Mellors, Robert Merrifield, Anna Minamide, Masashi Mori, Shuichi Muni Krishna, Kailasam Murchie, Scott Nguyen, Hanh Norman, Ryan Ojeda González, Arian Padma Kumari, B. Panning, Mark Pearce, Joshua Pierce, David Podolak, Morris Pope, Allen Prakash, Satya Price, Anthony Prochazka, Ivan Pudykiewicz, Januscz Raynolds, Martha Rodger, Craig Roy, Alexandre Roy, Christopher Scharien, Randall Schindelegger, Michael Sharma, Om Shirooka, Ryuichi Skordas, Efthimios Smart, Clara Smith, Jessica B. Stoppa, Francesco Sugiyama, Masahiro Takahashi, Hanii Tang, Guanglin Tian, Baijun Tivey, Maurice Trancoso, Ralph Trifonova, Neda Trigo-Rodriguez, Josep Tzeng, Mimi Uusitalo, Laura Valeo, Caterina Vaniman, David Ventrice, Michael Wang, Pei Wang, Shuguang Wong, Sun Xi, Baike Xu, Guangyu Xu, Kuan-man Yang, John Xun Yang, Song Yeh, Sang-Wook Yu, Yan Zeng, Z Zhai, Chengxing Zhang, Renyi Zhou, Jiangcun
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".