Bibliographic record
Abstract
Editorial| July 31, 2019 Hello from the New Editor‐in‐Chief Allison Bent Allison Bent Corresponding Author asrleditor@seismosoc.org Search for other works by this author on: GSW Google Scholar Author and Article Information Allison Bent Corresponding Author asrleditor@seismosoc.org Publisher: Seismological Society of America First Online: 31 Jul 2019 Online Issn: 1938-2057 Print Issn: 0895-0695 © Seismological Society of America Seismological Research Letters (2019) 90 (5): 1719–1720. https://doi.org/10.1785/0220190158 Article history First Online: 31 Jul 2019 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation Allison Bent; Hello from the New Editor‐in‐Chief. Seismological Research Letters 2019;; 90 (5): 1719–1720. doi: https://doi.org/10.1785/0220190158 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietySeismological Research Letters Search Advanced Search My first day as Editor‐in‐Chief (EIC) of SRL coincides with Canada Day. I will be taking the day off. I have no choice. It is the law. On my second day as EIC, I will be well rested and ready to get to work. I am excited about taking on this role. Granted, the excitement is tempered with the occasional moment of sheer terror when I wonder why I ever thought this was a good idea, but, on the whole, I am looking forward to what I hope will be an excellent adventure. SRL is my favorite journal. I have... You do not have access to this content, please speak to your institutional administrator if you feel you should have access.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.348 | 0.343 |
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".