Bibliographic record
Abstract
The Northern Eurasia Earth Science Partnership Initiative (NEESPI) was launched five years ago with the release of its Science Plan ( http://neespi.org ). Gradually, the Initiative was joined by numerous international projects and launched in the European Union, Russia, United States, Canada, Japan, and China. Currently, serving as an umbrella for more than 130 individual research projects (always with international participation) and with a $15M annual budget, this highly diverse initiative is in full swing. Since the first NEESPI focus issue (Pavel Groisman et al 2007 Environ. Res. Lett. 2 045008 (1pp) ) in December 2007, several NEESPI Workshops and Sessions at International Meetings have been held that strengthen the NEESPI grasp on biogeochemical cycle and cryosphere studies, climatic and hydrological modeling, and regional NEESPI components in the Arctic, non- boreal Eastern Europe, Central Asia, northern Siberia, and mountainous regions of the NEESPI domain. In May 2009, an overview NEESPI paper was published in the Bulletin of the American Meteorological Society (BAMS) (Pavel Groisman et al 2009 Bull. Am. Met. Soc. 90 671 ). This paper also formulated a requirement to the next generation of NEESPI studies to work towards attaining a higher level of integration of observation programs, process studies, and modeling, across disciplines. Three books devoted to studies in different regions of Northern Eurasia prepared by the members of the NEESPI team have appeared and/or are scheduled to appear in 2009. This (second) ERL focus issue dedicated to climatic and environmental studies in Northern Eurasia is composed mostly from the papers that were presented at two NEESPI Open Science Sessions at the Annual Fall Meeting of the American Geophysical Union (December 2008, San Francisco, CA) and at the General Assembly of the European Geosciences Union (April 2009, Vienna, Austria), as well as at the specialty NEESPI Workshops convened in Jena, Helsinki, Odessa, Urumqi, Krasnoyarsk, St Petersburg, and Bishkek during the past two years. As in the first NEESPI focus issue, papers that make up this second issue can be divided into five major topics: climate and hydrology; land cover and land use; the biogeochemical cycle and its feedbacks; cryosphere; human dimension. However, this partitioning is less rigid compared to the partitioning in the first Issue. Following the requirement of a higher level of integration outlined in the BAMS paper, many papers in this issue respond to two or even three of the topics listed above.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.007 |
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".