Bibliographic record
Abstract
Tracking progress in marine climatologyThis special section accumulates articles from the Fourth JCOMM Workshop on Advances in Marine Climatology (CLIMAR-IV) held in Asheville, NC, USA from 9 to 12 June 2014.Since the first workshop in Vancouver in 1999, CLIMAR Workshops track the progress in marine climatology comprehensively covering scientific, methodological and data management issues of marine climatology (see e.g.Gulev, 2005;Gulev and Woodruff, 2011).Marine climatological data play a crucial role in assessing past and ongoing climate variability and change.Without long-term global and regional time series of marine climatological data, we cannot track global climate and quantify the ocean's role in climate variability and change.Besides estimation of variability of surface state variables, marine climatological data allow for computation of surface air-sea fluxes -the language of ocean and atmosphere communication.Also marine climatological archives provide invaluable contribution to the data assimilated by modern reanalyses, some of which are going back now to the 19th century, documenting the dynamically consistent history of the climate system.Marine climatological records (e.g.information about winds and waves) are also widely used for estimation of extreme events over the ocean, including storminess, storm surges and extreme sea level rise.These phenomena are of great importance for all types of marine structures, including operations of marine carriers and offshore engineering.Finally, marine climatological data provide an important source of in situ information for validation of satellite measurements of surface meteorological variables.However to be useful for all mentioned purposes, marine climatological data need to be accurately assembled, quality controlled and supplied with estimates of all types of uncertainties.These include measurement errors associated with the accuracy of instruments and with historical changes in observational practices which may affect not only climatological means but also estimates of climate variability.In addition to measurement errors, marine climatological data are subject to sampling uncertainties, originating from space and time inhomogeneities of sampling density, collected by merchant ships primarily along the major shipping routes.Unless these uncertainties are properly estimated, marine climatological data sets are more difficult to be effectively used for any purpose.For many years, starting from the late 1980s marine climatological data are assimilated in the International Comprehensive Ocean-atmosphere Data Set (ICOADS), representing now a unique freely available digital archive
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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.036 | 0.017 |
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".