Bibliographic record
Abstract
An international workshop on sea-level measurements in hostile conditions was held 13– 15 March 2018 at the N.N. Zubov State Oceanographic Institute (SOI) of Roshydromet, Moscow, Russian Federation. Experts from 11 countries (Australia, Canada, Denmark, Finland, France, Germany, Norway, Russian Federation, Sweden, United Kingdom, and USA) presented to the workshop a total of 24 presentations. They are available from http://www.ioc-unesco.org/hostile-conditions-sea-level-workshop. The presentations and discussions at the workshop focused on problems of sea level measurements in regions exposed to several different kinds of adverse environmental impact. Such regions primarily include the coastal zones of the polar regions, as well as the seas covered with ice during winter. The workshop addressed the impacts of extreme events, such major storms and high wave conditions. The workshop also discussed new measurement systems and instrument protection technologies, together with methods for sustainable transmission of observational data. Recommendations of the workshop at SOI are available from: http://www.ioc-unesco.org/index.php?option=com_oe&task=viewDocumentRecord&docID=21507.
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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".