Bibliographic record
Abstract
PAGES does many things to try to knit the international, interdisciplinary paleoenvironmental community together.One of these activities is hosting visiting scientists in the International Project Office as a way to proactively enhance the interaction between the IPO and the many researchers involved in PAGES projects.Recent and planned visitors have included sabbatical visits from senior scientists, postdocs, and graduate students.They have come from home institutions in Canada, China, Ethiopia, Germany, India, Mexico, Morocco, Russia, South Africa, and the USA.PAGES has benefited enormously from these visitors' substantial efforts in hosting workshops, guest editing topical issues of this newsletter, editing books and special issues arising from PAGES' activities, developing new initiatives, and enhancing PAGES' visibility in their home countries.Senior visitors have benefited from an opportunity to get away from their home institution for a period to focus on a PAGES project or develop new interactions and lines of research.Younger scientists benefit from taking on interesting and challenging tasks that would not normally arise in their research and making many new contacts within the international PAGES network.We are now working hard in many different ways to improve our ability to host visiting scientists in the future.I strongly encourage any scientists interested in taking the opportunity to spend some time at the PAGES International Project Office in Bern, Switzerland to contact me.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.018 |
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".